Bibliographic record
Abstract
Dear community of geographers,What is critical geography, and what can, and must, it be?As a journal that embraces radical visions for the field, we embrace this question again and again.Now we write to announce a renewed vision for ACME that brings the journal into conversation with broader publics and further defines and advances our scholarly and activist commitments.We stand with you in the struggles against global injustice, and all the more so in this time of global pandemic.We appreciate the work you do to fight for change.We hope this finds you well and well connected to those you love.In March 2020, we announced that we were "Slowing Down in Solidarity" 1 with the urgent imperative to offer deeper care and support to our colleagues and comrades at the arrival of the pandemic, and then two months later issued a full "Pausing New Articles Unrelated to COVID-19 Pandemic-Related Issues." 2 At the three year anniversary of these announcements, we find that we are more in need of support and solidarity than ever.With this "Unpause-ish Statement," ACME's Editorial Collective affirms our 20-plus-year commitment to critical geography.We continue to oppose the many forms of violence, injustice, and inequity that disrupt and destabilize the lives of marginalized human and morethan-human beings around the world and the planet we inhabit.Accordingly, we clarify that a "return to business as usual" is impossible and unwarranted, and that it recreates the injustices we seek to fight.It is our position to maintain
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.043 | 0.047 |
| Insufficient payload (model declined to judge) | 0.033 | 0.027 |
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".