Editors' report: Reflections for the <i>Journal of Research in Science Teaching</i> (2015–2020)
Bibliographic record
Abstract
We have very recently concluded our full term of editing the Journal of Research in Science Teaching (JRST), the official journal of NARST: A Global Organization for Improving Science Education through Research.Our editorial team managed all incoming submissions for the 5-year period from 2015 through 2019, and was responsible for publishing five JRST volumes, from 2016 through 2020 (volumes 53 through 57).We now bring you this report to describe our work, highlight the outcomes of our 5-year editorship, and situate this work, with the benefit of hindsight, from the perspective of the Journal's recent history.We are the third team to have completed our editorship since JRST's key shift in 2005 to the online submission and review of manuscripts, supported by the ScholarOne Manuscripts™ system.The shift resulted in concomitant, substantial, and continuing increases in the number of JRST manuscript submissions and those coming from non-US based authors.The shift surely brought welcome and decisive advantages to JRST, as well as the challenge of managing a substantially increased yearly volume of submissions.Here, we use ScholarOne Manuscripts™ reporting tools, metrics provided by Wiley ® -the publisher of JRST, and indexing resources-especially, the Web of Science™, to shed light on some key patterns and performance metrics for JRST.We began our editorship with a firm commitment to uphold, deepen, and expand foundational dimensions of JRST.We identified a set of goals and initiatives in support of our vision and commitments.The Journal was entrusted to our care for the period of the editorship, passed to us from those who came before us, and we now have passed the care of JRST on to those who follow us in this endeavor, soon to celebrate its 60th year in 2023.
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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 | 0.079 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.035 | 0.017 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.026 | 0.026 |
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".