Whose Research is it Anyway? Academic Social Networks Versus Institutional Repositories
Bibliographic record
Abstract
INTRODUCTION Looking for ways to increase deposits into their institutional repository (IR), researchers at one institution started to mine academic social networks (ASNs) (namely, ResearchGate and Academia.edu) to discover which researchers might already be predisposed to providing open access to their work. METHODS Researchers compared the numbers of institutionally affiliated faculty members appearing in the ASNs to those appearing in their institutional repositories. They also looked at how these numbers compared to overall faculty numbers. RESULTS Faculty were much more likely to have deposited their work in an ASN than in the IR. However, the number of researchers who deposited in both the IR and at least one ASN exceeded that of those who deposited their research solely in an ASN. Unexpected findings occurred as well, such as numerous false or unverified accounts claiming affiliation with the institution. ResearchGate was found to be the favored ASN at this particular institution. DISCUSSION The results of this study confirm earlier studies’ findings indicating that those researchers who are willing to make their research open access are more disposed to do so over multiple channels, showing that those who already self-archive elsewhere are prime targets for inclusion in the IR. CONCLUSION Rather than seeing ASNs as a threat to IRs, they may be seen as a potential site of identifying likely contributors to the IR.
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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 | 0.042 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".