Open access in translation and interpreting studies: A bibliometric overview of its impact (1996-2015)
Bibliographic record
Abstract
Open access (OA) is now a complex multifaceted phenomenon and one of the hottest topics under debate extending to other actors beyond academia. OA has been growing lately not only for ethical or ideological reasons, but also due to the pressure of formal mandates from public research funders. Many studies have shown that OA research outputs have greater citation impact as compared to similar toll-access (TA) ones, thus introducing a more pragmatic dimension for scholars considering OA. However, other studies claim there are many confounding factors that often have not been taken into account. To date, no study has been carried out concerning open access citation advantage (OAA) in TIS (translation and interpreting studies). This paper contributes to this debate by carrying out a bibliometric analysis by comparing the performance of documents in terms of accrued citations depending on access type in order to find out whether OA TIS research is cited more than its TA counterpart. We based our analysis on a sample of more than 20,000 TIS-related documents extracted from BITRA, covering a time span of 20 years (1996-2015). The main conclusion is that, although OA publications tend to be cited slightly more often than TA documents in our period of study, this difference is too small to either support or reject the OAA hypothesis in TIS.
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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 | 0.031 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.150 | 0.290 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".