<i>Academia Letters</i>: Examination of an ‘Experimental’ Academia.edu Publishing Model
Bibliographic record
Abstract
This article makes a historical assessment of a publishing ‘experiment’ that started in 2020 and ended in 2022 by Academia.edu , a popular academic social network site, that took the form of a peer-reviewed ‘journal,’ Academia Letters. The authors discovered a publicly hidden open-access cost, as an article processing charge of US$500, some inconsistencies or ambiguities in select editorial policies, the lack of an editorial board, and the absence of an integrity and publishing ethics policy, cumulatively indicating that this publishing model was lacking some basic, robust scholarly indices that are typically found in conventional peer-reviewed journals. Despite its short two-year history, about 4500 papers were published in Academia Letters, suggesting that this publishing model was nonetheless attractive and popular. This overview of Academia Letters will allow Academia.edu and other academic publishers to reflect on specifics or weaknesses of this publishing model before using it in the future to ensure trustworthy scholarly communication in the academic community.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrityScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchScholarly communicationResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.088 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".