A Challenge to Metrics as Evidence of Scholarity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it