Bibliographic record
Abstract
"Compiling key works of critical geographies is an impossible task. We began this task by conducting a survey among the editorial board members of the journal ACME: An E-Journal of Critical Geographies, asking their opinion for the three most important texts of critical geographies. We anticipated a few key works to emerge from this survey, which we would then include in this collection. However, the survey revealed that no consensus existed among ACME board members and other colleagues we consulted on what the key texts in the field are. In fact, not a single text was even mentioned twice by the survey participants. Furthermore, the idea of compiling “key” texts of critical geographies is highly problematic. Initially, we intended to call this collection “A Reader in Critical Geographies.” A colleague, who heard about our intention, remarked that a “reader” suggests “authorization and authority” and cynically added: “Not exactly the natural positions of „critical geography‟...”. We began to realize the impossibility of our project. Yet, we decided to continue with it because critical scholarship to us means precisely to engage with the contradictions of our discipline at the practical level." -- from p.viii
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".