A Challenge to Metrics as Evidence of Scholarity
Bibliographic record
Abstract
Now that universities have shifted their priorities to those of the ‘cash nexus’, they increasingly articulate their accomplishments and validate their existence in business terms for a globally competitive academic market. But corporatizing trends and the use of bibliometric tools that rank publication and quantify scholarity impact a redefinition and reconceptualization of what it means to be a scholar as an instrument of the corporate regime. Judith Butler's notion of normalizing categories is the lens through which this article examines European and United Kingdom corporatizing strategies such as the Bologna Process and Research Assessment Exercise. Key to the discussion is a critique of bibliometric accountability mechanisms that privilege quantification and promote academic materialism. These devices become normative, and, thus, the definition of scholarity to which academics default.
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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 | 0.215 | 0.556 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.029 | 0.058 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.007 | 0.014 |
| 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; 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".