If you do not <i>deign</i> to quantify, someone else will do it for you: In support of a balanced approach to the evaluation of science
Bibliographic record
Abstract
This is a commentary in support of Olof Hallonsten’s historical-sociological argument for countering the growing distrust and governance of science. From this starting point, the problem of quantification in the evaluation of science is addressed and several examples of the unintended consequences of the currently available metrics are discussed. In particular, the issue of quantification is discussed in regard to the modality of scientific research, power and research and the peer relationship. Although in approval with Hallonsten’s argument for reversing the burden of proof, reasonable skepticism is expressed regarding the persuasiveness that this counter-rhetoric will have on members of parliament, public servants and university administrators. If this long-term goal is to be accomplished, it is argued that concrete actions must be pursued in the short and medium term. In this spirit, several suggestions are formulated to further this agenda, most notably greater support for intellectual diversity, greater participation and readership in science studies by science practitioners and the promotion of the comparative approach for understanding the different ways that metrics are actually used in practice. Finally, I argue that the refusal of participating in the quantification of science is bound to hinder applied critical thinking and will most likely and regrettably exacerbate its current perverse effects.
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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.025 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".