Ideational Influence, Connectedness, and Venue Representation: Making an Assessment of Scholarly Capital
Bibliographic record
Abstract
Assessing the research capital that a scholar has accrued is an essential task for academic administrators, funding agencies, and promotion and tenure committees worldwide. Scholars have criticized the existing methodology of counting papers in ranked journals and made calls to replace it (Adler & Harzing, 2009; Singh, Haddad, & Chow, 2007). In its place, some have made calls to assess the uptake of a scholar’s work instead of assessing “quality” (Truex, Cuellar, Takeda, & Vidgen, 2011a). We identify three dimensions of scholarly capital (ideational influence (who uses one’s work?), connectedness (with whom does one work?) and venue representation (where does one publish their work?)) in this paper as part of a scholarly capital model (SCM). We develop measurement models for the three dimensions of scholarly capital and test the relationships in a path model. We show how one might use the measures to evaluate scholarly research activity.
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How this classification was reachedexpand
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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".