Interviews with editors of library science journals on transitioning to open access
Bibliographic record
Abstract
These three files are related to qualitative, semi-structured interviews conducted in Fall 2023 with editors of Library and Information Science (LIS) journals on transitioning to open access. One subgroup consisted of participants who were editors at the time of an LIS journal when it transitioned (or flipped) to an open access model that does not charge a fee to either readers or authors (which this study refers to as equitable open access), and the other subgroup consisted of current editors (at the time) of LIS journals that have not yet transitioned (or unflipped) to an equitable open access model. Two of the files are the interview protocols for each group of flipped and unflipped editors, and the third file is the codebook the researchers used to analyze the interview transcripts. Interview transcripts are not being publicly shared to ensure confidentiality for interview participants. The interview protocols were created based on the findings of a prior research study: Borchardt, R., Dawson, D., & Schultz, T. (2024). Financial and other perceived barriers to transitioning to an equitable no-publishing fee open access model: A survey of LIS journal editors. College & Research Libraries, 85(1). https://doi.org/10.5860/crl.85.1.96 The codebook was created iteratively based on the researchers' review and analysis of the interview transcripts.
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.
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 | Scholarly communicationOpen science Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".