Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine academics’ awareness of and attitudes towards Wikipedia and Open Access journals for academic publishing to better understand the perceived benefits and challenges of these models. Design/methodology/approach – Bases for analysis include comparison of the models, enumeration of their advantages and disadvantages, and investigation of Wikipedia's web structure in terms of potential for academic publishing. A web survey was administered via department-based invitations and listservs. Findings – The survey results show that: Wikipedia has perceived advantages and challenges in comparison to the Open Access model; the academic researchers’ increased familiarity is associated with increased comfort with these models; and the academic researchers’ attitudes towards these models are associated with their familiarity, academic environment, and professional status. Research limitations/implications – The major limitation of the study is sample size. The result of a power analysis with GPower shows that authors could only detect big effects in this study at statistical power 0.95. The authors call for larger sample studies that look further into this topic. Originality/value – This study contributes to the increasing interest in adjusting methods of creating and disseminating academic knowledge by providing empirical evidence of the academics’ experiences and attitudes towards the Open Access and Wikipedia publishing models. This paper provides a resource for researchers interested in scholarly communication and academic publishing, for research librarians, and for the academic community in general.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".