Whose Research is it Anyway? Academic Social Networks Versus Institutional Repositories
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
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.
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.
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 | 0.008 | 0.001 |
| 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 | 0.010 | 0.137 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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 it