Cultural Mapping as a Social Practice: A Response to "Mapping the Cultural Boundaries in Schools and Communities: Redefining Spaces Through Organizing"
Bibliographic record
Abstract
Inspired by Gerald Wood and Elizabeth Lemley’s (2015) article entitled Mapping the Cultural Boundaries in Schools and Communities: Redefining Spaces Through Organizing, this response inquires further into cultural mapping as a social practice. From our perspective, cultural mapping has potential to contribute to place making, as well as the values to sustain more equitable social futures. Thus, alongside the maps created, we longed to learn more about how the participants were engaged in mapping, how perceptions of mapping changed over time and context, how participation was mediated by relationships, and how transformation in the participants, child, youth, and adults was manifested. Making visible the richness of this experience, however, likely requires research funding, support, and time.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.049 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.028 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".