Assessing the utility of an institutional publications officer: a pilot assessment
Bibliographic record
Abstract
BACKGROUND: The scholarly publication landscape is changing rapidly. We investigated whether the introduction of an institutional publications officer might help facilitate better knowledge of publication topics and related resources, and effectively support researchers to publish. METHODS: In September 2015, a purpose-built survey about researchers' knowledge and perceptions of publication practices was administered at five Ottawa area research institutions. Subsequently, we publicly announced a newly hired publications officer (KDC) who then began conducting outreach at two of the institutions. Specifically, the publications officer gave presentations, held one-to-one consultations, developed electronic newsletter content, and generated and maintained a webpage of resources. In March 2016, we re-surveyed our participants regarding their knowledge and perceptions of publishing. Mean scores to the perception questions, and the percent of correct responses to the knowledge questions, pre and post survey, were computed for each item. The difference between these means or calculated percentages was then examined across the survey measures. RESULTS: 82 participants completed both surveys. Of this group, 29 indicated that they had exposure to the publications officer, while the remaining 53 indicated they did not. Interaction with the publications officer led to improvements in half of the knowledge items (7/14 variables). While improvements in knowledge of publishing were also found among those who reported not to have interacted with the publications officer (9/14), these effects were often smaller in magnitude. Scores for some publication knowledge variables actually decreased between the pre and post survey (3/14). Effects for researchers' perceptions of publishing increased for 5/6 variables in the group that interacted with the publications officer. DISCUSSION: This pilot provides initial indication that, in a short timeframe, introducing an institutional publications officer may improve knowledge and perceptions surrounding publishing. This study is limited by its modest sample size and temporal relationship between the introduction of the publications officer and changes in knowledge and perceptions. A randomized trial examining the publications officer as an effective intervention is needed.
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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 | 0.055 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".