Advice for New Authors about the Submission of Articles / Conseils pour les nouveaux auteurs sur la soumission d’articles
Bibliographic record
Abstract
ABSTRACTNew authors often see the publication process as a mystery that only gets revealed in bits and pieces over time. This article aims to present some tips and ideas to new authors to facilitate the submission of an article to Canadian Journal of Nonprofit and Social Economy Research / Revue canadienne de recherche sur les OSBL et l’économie sociale (ANSERJ). It describes the review process and highlights some key milestones. As the English Language and French Language editors for ANSERJ, we would like to encourage new contributors, and thus we will highlight specific items as they apply to ANSERJ. These guidelines complement the author guidelines already posted on the ANSERJ website. Our advice may apply to authors interested in other journals with a peer review process. RÉSUMÉLes nouveaux auteurs considèrent souvent le processus de publication comme un mystère qui se découvre au fil du temps. Cet article vise à présenter certains conseils et réflexions pour faciliter la soumission d’un article à la Revue canadienne de recherche sur les OSBL et l’économie sociale / Canadian Journal of Nonprofit and Social Economy Research (ANSERJ). Il décrit le processus de publication et ses étapes principales. À titre de rédacteurs en chef d’ANSERJ, nous aimerions encourager les nouveaux chercheurs, contribuer au débat par quelques conseils et réflexions et souligner certains éléments spécifiques à notre revue. Les présentes réflexions complètent les directives déjà présentes sur le site web de la revue. Ils peuvent s’appliquer à des auteurs intéressés par d’autres revues avec comité de lecture.
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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 | 0.115 | 0.502 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.105 | 0.150 |
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".