Advancing children’s geographies through ‘grey areas’ of age and childhood
Bibliographic record
Abstract
Abstract Children’s Geographies actively engages with critical understandings of age‐based research. However, the concept of age is an uncritical entity in these studies fueled by western concepts of childhood. Demarcating age numerically runs the risk of measuring childhoods in the Majority Worlds with Minority World concepts that are not culturally sensitive, but also forecloses any innovations. Researchers often spend so long debating definitions and boundaries when it is often in the grey areas of scholarship and life that the most exciting events and outcomes occur. This review begins with navigating ‘grey areas of age’ from how it is often measured in spaces of conceptual ambiguity with regards to experiences of being in between formal definitions of childhood or ‘Children’s Geographies.’ This is elaborated upon from Eurocentric linages that have shaped the subfield of Children’s Geographies, and in which the subfield should continue shifting from to further decolonize Eurocentric research related to age and childhood. The paper ends by presenting ways of further advancing the subfield of Children’s Geographies through (inter)generational positioning concepts and new interdisciplinary life course studies that further nuances the social variable of age as a grey area in the subfield.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".