Canadian and South African Scholars’ Use of Institutional Repositories, ResearchGate, and Academia.edu
Bibliographic record
Abstract
Since their initial development in the early 2000s, institutional repositories (IRs) have proliferated around the globe. Due to low faculty participation, however, content recruitment has often posed a significant challenge for librarians and others promoting their use. Through the last decade, academic social networks (ASNs), such as ResearchGate and Academia.edu, have become popular among scholars as a means to communicate with each other and share their research. Semi-structured interviews were conducted with sixty scholars at six universities in Canada and South Africa to explore their views and practices pertaining to IRs and ASNs. Interviews were transcribed and coded to elucidate trends and themes in the data. The study found that few participants were active supporters of their local IRs. Lack of awareness, time limitations, and concerns regarding copyright remain some of the main obstacles to increased faculty participation. Conversely, more than half of the interviewees were active users of either ResearchGate or Academia.edu. These users valued ASNs both as a means of sharing their work and as tools facilitating connections with their colleagues internationally. Though IRs need not compete with these networks, proponents of open access repositories should be prepared to explain to faculty why they should consider having their research made accessible in a repository though they may already actively share their work through ResearchGate or Academia.edu. Significantly, both ASNs and IRs were more popular among South African than Canadian researchers. It is hoped that the results of the study will be helpful in informing the understanding and decisions of librarians and others working to develop and promote IRs and green open access more broadly.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".