2021 JGS best paper award and the editors’ choice paper volume 24(1)
Bibliographic record
Abstract
With the first issue of 2022, it is our pleasure to continue two initiatives started in 2020 to acknowledge and celebrate the outstanding quality of research published in the journal.First, the annual JGS Best Paper Award, and second, the Editors' Choice of their favourite paper of each issue.The members of the editorial board of the journal and/or the editors may nominate candidates for the JGS Best Paper award.Following the nominations, the decision about the award rests with the editors-in-chief of the journal.The winners of the yearly award and the authors of the quarterly editors' choice articles are listed on the website of the journal, alongside previous awardees.Their respective papers are made free access for a certain amount of time.The objective of the JGS Best Paper Award is to encourage and recognize excellent scholarship published in the journal in the preceding year.For the year 2021, one excellent paper clearly stands out.It properly represents the broad scope of a journal that covers the fields of GIScience and spatial planning, as well as spatial statistics and econometrics.In 2021 JGS published 22 papers, all undergoing a stringent peer review process.All of them are of excellent quality, making any decision about prizes normally a difficult one.Choosing among this pool of top research in our field this time was not difficult at all: The scientific community recognized the Open Access paper published online first on August 8, 2020 with outstanding attention in terms of accesses, citations and online attention.Therefore, it is our great pleasure to announce that the winner of the 2021 JGS Best Paper Award is the contribution by Chris Brunsdon and Alexis Comber, already acknowledged as Editors' Choice Paper Volume 23(4).Their respective original research paper is summarized next and can be accessed in full via the Journal's homepage (DOI is given below).
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
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.361 | 0.304 |
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".