THE REVIEWING PROCESS FOR ISPRS EVENTS
Bibliographic record
Abstract
Abstract. Following the first initiatives taken by the International Programme Committee of the XXIIIrd ISPRS Congress in Prague (Czech Republic) in 2016, modifications of the reviewing process of ISPRS events were further considered during the years 2017 and 2018. This evolution first targets to better fit such a process to the currents requirements and expectations of the ISPRS community. Secondly, it aims to provide unified guidelines for the different steps of the process. Under the aegis of the 2020 Congress Director and ISAC (International Science Advisory Committee) chair, several discussions were held in-between September 2017 and June 2018 with ISAC members, Technical Commission Presidents (TCP), council members, 2016 and 2020 Congress Programme Chairs. This document serves as a unique transparent basis that applies for all kinds of ISPRS events (from Congress and Geospatial Week to smaller workshops), and all categories of people that are bound to be involved in the evaluation process of scientific contributions (authors, reviewers, TCPs, … ). It also specifies the evaluation criteria for the works submitted to ISPRS events, both for full papers and abstracts. Subsequently, it helps authors to improve the content and shape of their contributions. Eventually, this paper is targeted to help new chairs to smoothly prepare their future event. The following guidelines were first adopted for the 2018 Technical Commission Symposia.
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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 | 0.098 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.138 |
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".