Looking Beyond the Pointing Finger: Ensuring the Success of the Scholarly Capital Model in the Contemporary Academic Environment
Bibliographic record
Abstract
The currently predominant method of counting articles in ranked venues (CARV) to assess one’s academic achievements has had a deleterious impact on the state of the IS field, which points to a need for a paradigm shift. In this rejoinder to Cuellar, Truex, and Takeda’s (2019) article, I extend the scholarly capital model that they propose and comment on its applicability, adoption, and potential misuse. I propose that the model would benefit if it included a new component – practical capital, which comprises three dimensions: knowledge outreach (a scholar’s direct contribution to professional forums), knowledge impact (a scholar’s indirect contribution to professional forums), and community engagement (a scholar’s connections with the non-academic sector). I strongly recommend that the Association for Information Systems accept a formal stewardship role and facilitate further development, testing, and promotion of the scholarly capital model.
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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 | 0.135 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.040 | 0.062 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".