Why numerical ratings?

Ultimately, we're trying to replace the question of "what tier of journal did a paper get into?" with "how highly was the paper rated?" We believe this is a more valuable metric. It can be more fine-grained. It should be less prone to gaming. It aims to reduce randomness in the process, through things like 'the availability of journal space in a particular field'. See our discussion of Reshaping academic evaluation: beyond the binary... .

To get to this point, we need to have academia and stakeholders see our evaluations as meaningful. We want the evaluations to begin to have some value that is measurable in the way “publication in the AER” is seen to have value.

While there are some ongoing efforts towards journal-independent evaluation, these tend not use comparable metrics. Typically, they either have simple tick-boxes (like "this paper used correct statistical methods: yes/no") or they enable descriptive evaluation without an overall rating. As we are not a journal, and we don’t accept or reject research, we need another way of assigning value. We are working to determine the best way of doing this through quantitative ratings. We hope to be able to benchmark our evaluations to "traditional" publication outcomes. Thus, we think it is important to ask for both an overall quality rating and a journal ranking tier prediction.

Why these categories?

In addition to the overall assessment, we think it will be valuable to have the papers rated according to several categories. This could be particularly helpful to practitioners who may care about some concerns more than others. It also can be useful to future researchers who might want to focus on reading papers with particular strengths. It could be useful in meta-analyses, as certain characteristics of papers could be weighed more heavily. We think the use of categories might also be useful to authors and evaluators themselves. It can help them get a sense of what we think research priorities should be, and thus help them consider an overall rating.

However, these ideas have been largely ad-hoc and based on the impressions of our management team (a particular set of mainly economists and psychologists). The process is still being developed. Any feedback you have is welcome. For example, are we overemphasizing certain aspects? Are we excluding some important categories?

We are also researching other frameworks, templates, and past practice; we hope to draw from validated, theoretically grounded projects such as RepliCATS.

Why ask for credible intervals?

In eliciting expert judgment, it is helpful to differentiate the level of confidence in predictions and recommendations. We want to know not only what you believe, but how strongly held your beliefs are. If you are less certain in one area, we should weigh the information you provide less heavily in updating our beliefs. This may also be particularly useful for practitioners. Obviously, there are challenges to any approach. Even experts in a quantitative field may struggle to convey their own uncertainty. They may also be inherently "poorly calibrated" (see discussions and tools for calibration training). Some people may often be "confidently wrong." They might state very narrow "credible intervals", when the truth—where measurable—routinely falls outside these boundaries. People with greater discrimination may sometimes be underconfident. One would want to consider and potentially correct for poor calibration. As a side benefit, this may be interesting for research in and of itself, particularly as The Unjournal grows. We see 'quantifying one's own uncertainty' as a good exercise for academics (and everyone) to engage in.

"Weightings" for each rating category (removed for now)

Adjustments to metrics and guidelines/previous presentations

Pre-October 2023 'ratings with weights' table, provided for reference (no longer in use)

We had included the note:

We give the previous weighting scheme in a fold below for reference, particularly for those reading evaluations done before October 2023.

As well as:

Suggested weighting: 0. Why 0?

Elsewhere in that page we had noted:

As noted above, we give suggested weights (0–5) to suggest the importance of each category rating to your overall assessment, given The Unjournal's priorities. But you don't need, and may not want to use these weightings precisely.

The weightings were presented once again along with each description in the section "Category explanations: what you are rating".

Pre-2024 ratings and uncertainty elicitation, provided for reference (no longer in use)

[FROM PREVIOUS GUIDELINES:]

You may feel comfortable giving your "90% confidence interval," or you may prefer to give a "descriptive rating" of your confidence (from "extremely confident" to "not confident").

Quantify how certain you are about this rating, either giving a 90% confidence/credibility interval or using our scale described below. (We prefer the 90% CI. Please don't give both.

[Previous...] Remember, we would like you to give a 90% CI or a confidence rating (1–5 dots), but not both.

And, for the 'journal tier' scale:

Previous 'descriptions of ratings intervals'

[Previous guidelines]: The description folded below focuses on the "Overall Assessment." Please try to use a similar scale when evaluating the category metrics.

See also

#more-reliable-precise-and-useful-metrics This page explains the value of the metrics we are seeking from evaluators.

Unjournal Evaluator Guidelines and Metrics - Discussion space

How to write a good review (general conventional guidelines)

We're happy for you to use whichever process and structure you feel comfortable with when writing your evaluation content.

Writing referee reports: resources and benchmarks

Economics How to Write an Effective Referee Report and Improve the Scientific Review Process (Berk et al, 2017)

Semi-relevant: Econometric Society: Guidelines for referees

Report: Improving Peer Review in Economics: Stocktaking and Proposal (Charness et al 2022)

Open Science

PLOS (Conventional but open access; simple and brief)

Peer Community In... Questionnaire (Open-science-aligned; perhaps less detail-oriented than we are aiming for)

Open Reviewers Reviewer Guide (Journal-independent “PREreview”; detailed; targets ECRs)

General, other fields

The Wiley Online Library (Conventional; general)

"Peer review in the life sciences (Fraser)" (extensive resources; only some of this is applicable to economics and social science)

Other templates and tools

Collaborative template: RRR assessment peer review

Introducing Structured PREreviews on PREreview.org

‘the 4 validities’ and seaboat

We are considering asking evaluators, with compensation, to assist and engage in the process of "robustness replication." This may lead to some interesting follow-on possibilities as we build our potential collaboration with the Institute for Replication and others in this space.

We might ask evaluators discussion questions like these:

• What is the most important, interesting, or relevant substantive claim made by the authors, (particularly considering global priorities and potential interventions and responses)?

• What statistical test or evidence does this claim depend on, according to the authors?

• How confident are you in the substantive claim made?

• "Robustness checks": What specific statistical test(s) or piece(s) of evidence would make you substantially more confident in the substantive claim made?

• If a robustness replication "passed" these checks, how confident would you be then in the substantive claim? (You can also express this as a continuous function of some statistic rather than as a binary; please explain your approach.)

Background:

The Institute for Replication is planning to hire experts to do "robustness-replications" of work published in a top journal in economics and political science. Code- and data sharing is now being enforced in many or all of these journals and other important outlets. We want to support their efforts and are exploring collaboration possibilities. We are also considering how to best guide potential future robustness replication work.

You can download a pdf version of these guidelines here (updated March 2024).

What we'd like you to do

1. Write an evaluation of the target paper or project, similar to a standard, high-quality referee report. Please identify the paper's main claims and carefully assess their validity, leveraging your own background and expertise.

2. Give quantitative metrics and predictions as described below.

3. Answer a short questionnaire about your background and our processes.

Writing the evaluation (aka 'the review')

In writing your evaluation and providing ratings, please consider the following.

The Unjournal's expectations and criteria

In many ways, the written part of the evaluation should be similar to a report an academic would write for a traditional high-prestige journal (e.g., see some 'conventional guidelines' here). Most fundamentally, we want you to use your expertise to critically assess the main claims made by the authors. Are the claims well-supported? Are the assumptions believable? Are the methods are appropriate and well-executed? Explain why or why not.

However, we'd also like you to pay some consideration to our priorities:

1. Advancing our knowledge and practice

2. Justification, reasonableness, validity, and robustness of methods

3. Logic and communication

4. Open, communicative, replicable science

See our guidelines below for more details on each of these. Please don't structure your review according to these metrics, just pay some attention to them.

If you have questions about the authors’ work, you can ask them anonymously: we will facilitate this.

We want you to evaluate the most recent/relevant version of the paper/project that you can access. If you see a more recent version than the one we shared with you, please let us know.

Target audiences

We designed this process to balance three considerations with three target audiences. Please consider each of these:

1. Crafting evaluations and ratings that help researchers and policymakers judge when and how to rely on this research. For Research Users.

2. Ensuring these evaluations of the papers are comparable to current journal tier metrics, to enable them to be used to determine career advancement and research funding. For Departments, Research Managers, and Funders.

3. Providing constructive feedback to Authors.

We discuss this, and how it relates to our impact and "theory of change", here.

Quantitative metrics

We ask for a set of nine quantitative metrics. For each metric, we ask for a score and a 90% credible interval. We describe these in detail below. (We explain why we ask for these metrics here.)

Percentile rankings

For some questions, we ask for a percentile ranking from 0-100%. This represents "what proportion of papers in the reference group are worse than this paper, by this criterion". A score of 100% means this is essentially the best paper in the reference group. 0% is the worst paper. A score of 50% means this is the median paper; i.e., half of all papers in the reference group do this better, and half do this worse, and so on.

Here* the population of papers should be all serious research in the same area that you have encountered in the last three years.

Midpoint rating and credible intervals

For each metric, we ask you to provide a 'midpoint rating' and a 90% credible interval as a measure of your uncertainty. Our interface provides slider bars to express your chosen intervals:

The table below summarizes the percentile rankings.

Overall assessment

Percentile ranking (0-100%)

Judge the quality of the research heuristically. Consider all aspects of quality, credibility, importance to knowledge production, and importance to practice.

Claims, strength and characterization of evidence **

Do the authors do a good job of (i) stating their main questions and claims, (ii) providing strong evidence and powerful approaches to inform these, and (iii) correctly characterizing the nature of their evidence?

Methods: Justification, reasonableness, validity, robustness

Percentile ranking (0-100%)

Are the methods used well-justified and explained; are they a reasonable approach to answering the question(s) in this context? Are the underlying assumptions reasonable?

Are the results and methods likely to be robust to reasonable changes in the underlying assumptions? Does the author demonstrate this?

Avoiding bias and questionable research practices (QRP): Did the authors take steps to reduce bias from opportunistic reporting and QRP? For example, did they do a strong pre-registration and pre-analysis plan, incorporate multiple hypothesis testing corrections, and report flexible specifications?

Advancing our knowledge and practice

Percentile ranking (0-100%)

To what extent does the project contribute to the field or to practice, particularly in ways that are relevant to global priorities and impactful interventions?

(Applied stream: please focus on ‘improvements that are actually helpful’.)

Do the paper's insights inform our beliefs about important parameters and about the effectiveness of interventions?

Does the project add useful value to other impactful research?

We don't require surprising results; sound and well-presented null results can also be valuable.

Logic and communication

Percentile ranking (0-100%)

Are the goals and questions of the paper clearly expressed? Are concepts clearly defined and referenced?

Is the reasoning "transparent"? Are assumptions made explicit? Are all logical steps clear and correct? Does the writing make the argument easy to follow?

Are the conclusions consistent with the evidence (or formal proofs) presented? Do the authors accurately state the nature of their evidence, and the extent it supports their main claims?

Are the data and/or analysis presented relevant to the arguments made? Are the tables, graphs, and diagrams easy to understand in the context of the narrative (e.g., no major errors in labeling)?

Open, collaborative, replicable research

Percentile ranking (0-100%)

This covers several considerations:

Replicability, reproducibility, data integrity

Would another researcher be able to perform the same analysis and get the same results? Are the methods explained clearly and in enough detail to enable easy and credible replication? For example, are all analyses and statistical tests explained, and is code provided?

Is the source of the data clear?

Is the data made as available as is reasonably possible? If so, is it clearly labeled and explained??

Consistency

Do the numbers in the paper and/or code output make sense? Are they internally consistent throughout the paper?

Useful building blocks

Do the authors provide tools, resources, data, and outputs that might enable or enhance future work and meta-analysis?

Relevance to global priorities, usefulness for practitioners**

Are the paper’s chosen topic and approach likely to be useful to global priorities, cause prioritization, and high-impact interventions?

Does the paper consider real-world relevance and deal with policy and implementation questions? Are the setup, assumptions, and focus realistic?

Do the authors report results that are relevant to practitioners? Do they provide useful quantified estimates (costs, benefits, etc.) enabling practical impact quantification and prioritization?

Do they communicate (at least in the abstract or introduction) in ways policymakers and decision-makers can understand, without misleading or oversimplifying?

Journal ranking tiers

To help universities and policymakers make sense of our evaluations, we want to benchmark them against how research is currently judged. So, we would like you to assess the paper in terms of journal rankings. We ask for two assessments:

1. a normative judgment about 'how well the research should publish';

2. a prediction about where the research will be published.

Journal ranking tiers are on a 0-5 scale, as follows:

• 0/5: "Won't publish/little to no value". Unlikely to be cited by credible researchers

• 1/5: OK/Somewhat valuable journal

• 2/5: Marginal B-journal/Decent field journal

• 3/5: Top B-journal/Strong field journal

• 4/5: Marginal A-Journal/Top field journal

• 5/5: A-journal/Top journal

We encourage you to consider a non-integer score, e.g. 4.6 or 2.2.

As before, we ask for a 90% credible interval.

What journal ranking tier should this work be published in?

Journal ranking tier (0.0-5.0)

Assess this paper on the journal ranking scale described above, considering only its merit, giving some weight to the category metrics we discussed above.

Equivalently, where would this paper be published if:

1. the journal process was fair, unbiased, and free of noise, and that status, social connections, and lobbying to get the paper published didn’t matter;

2. journals assessed research according to the category metrics we discussed above.

What journal ranking tier will this work be published in?

Journal ranking tier (0.0-5.0)

The midpoint and 'credible intervals': expressing uncertainty

What are we looking for and why?

We want policymakers, researchers, funders, and managers to be able to use The Unjournal's evaluations to update their beliefs and make better decisions. To do this well, they need to weigh multiple evaluations against each other and other sources of information. Evaluators may feel confident about their rating for one category, but less confident in another area. How much weight should readers give to each? In this context, it is useful to quantify the uncertainty.

But it's hard to quantify statements like "very certain" or "somewhat uncertain" – different people may use the same phrases to mean different things. That's why we're asking for you a more precise measure, your credible intervals. These metrics are particularly useful for meta-science and meta-analysis.

You are asked to give a 'midpoint' and a 90% credible interval. Consider this as the smallest interval that you believe is 90% likely to contain the true value. See the fold below for further guidance.

Claim identification, assessment, and implications

We are now asking evaluators for “claim identification and assessment” where relevant. This is meant to help practitioners use this research to inform their funding, policymaking, and other decisions. It is not intended as a metric to judge the research quality per se. This is not required but we will reward this work.

See guidelines and examples here.

Survey questions

Lastly, we ask evaluators about their background, and for feedback about the process.

Other guidelines and notes

Length/time spent: This is up to you. We welcome detail, elaboration, and technical discussion.

Our data protection statement is linked here.