The Journal Impact Factor and its discontents: steps toward responsible metrics and better research assessment
Bibliographic record
Abstract
A small, self-selected discussion group was convened to consider issues surrounding impact factors at the first meeting of the Open Scholarship Initiative in Fairfax, Virginia, USA, in April 2016, and focused on the uses and misuses of the Journal Impact Factor (JIF), with a particular focus on research assessment. The group’s report notes that the widespread use, or perceived use, of the JIF in research assessment processes lends the metric a degree of influence that is not justified on the basis of its validity for those purposes, and retards moves to open scholarship in a number of ways. The report concludes that indicators, including those based on citation counts, can be combined with peer review to inform research assessment, but that the JIF is not one of those indicators. It also concludes that there is already sufficient information about the shortcomings of the JIF, and that instead actions should be pursued to build broad momentum away from its use in research assessment. These actions include practical support for the San Francisco Declaration on Research Assessment (DORA) by research funders, higher education institutions, national academies, publishers and learned societies. They also include the creation of an international “metrics lab” to explore the potential of new indicators, and the wide sharing of information on this topic among stakeholders. Finally, the report acknowledges that the JIF may continue to be used as one indicator of the quality of journals, and makes recommendations how this should be improved.OSI2016 Workshop Question: Impact FactorsTracking the metrics of a more open publishing world will be key to selling “open” and encouraging broader adoption of open solutions. Will more openness mean lower impact, though (for whatever reason—less visibility, less readability, less press, etc.)? Why or why not? Perhaps more fundamentally, how useful are impact factors anyway? What are they really tracking, and what do they mean? What are the pros and cons of our current reliance on these measures? Would faculty be satisfied with an alternative system as long as it is recognized as reflecting meaningfully on the quality of their scholarship? What might such an alternative system look like?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.671 | 0.829 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.049 | 0.052 |
| Science and technology studies | 0.026 | 0.066 |
| Scholarly communication | 0.124 | 0.100 |
| Open science | 0.015 | 0.038 |
| Research integrity | 0.030 | 0.069 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".