Rejoinder to the Response to "The Scholarly Capital Model"
Bibliographic record
Abstract
Crowston (2016) makes several criticisms of “the scholarly capital model”. In sum, he argues that we fail to develop novel measures, continue the worst aspects of the current system in terms of encouraging co-authorships with old boys, reinforce journal list fetishes, and that the SCM still provides ample ways to game the system. In response to his criticisms, we reaffirm that SCM’s aims to address the question “does this scholar possess sufficient scholarly capital to enable our organization to achieve its research goals?”. We argue that examining the research capital that a scholar brings to the organization is an improvement over the current method of evaluating scholars based on their number of publications in ranked journals. The profile of measures that we propose, while not as novel as altmetrics, encourages widespread co-authorships, de-centers the journal lists, and, thus, eliminates the journal fetishism and ecological fallacy present in the current system.
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 | 0.039 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.028 | 0.065 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".