Approaches to assessing impacts in the humanities and social sciences: recommendations from the Canadian research community
Bibliographic record
Abstract
Conversations about the assessment of scholarly impacts are frequently hindered by uncertainty, anxiety, or suspicion. Peter Severinson reports on work published by the Federation for the Humanities and Social Sciences in Canada that it is hoped will provide guidance to university administrators, public servants, and other members of the research community undertaking the demanding work of impact assessment. Efforts to assess scholarly impacts must account for the great diversity of scholarly work, use a broad definition of “impact”, employ a diverse mix of indicators, and ensure that researchers themselves play a leading role in selecting those indicators that best suit their work.
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 | 0.279 | 0.333 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.073 | 0.075 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.049 | 0.038 |
| Open science | 0.018 | 0.027 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".