Contextualizing Positionality, Intersectionality, and Intelligence in the Anthropocene
Bibliographic record
Abstract
The geological epoch of the Anthropocene has challenged traditional definitions of what intellectual abilities are necessary to creatively problem-solve, understand, and address contemporary societal and environmental crises. If we hope to make meaningful changes to how our society addresses these complex issues and pave the way for a better future for generations to come, we must advance traditional theories and measures of higher-order abilities to reflect equity and inclusion. To this end, we must address global issues by integrating the complexities of intersectional identities as they impact our understanding of what constitutes intelligence in individuals, groups, and diverse communities. This re-envisioning of intelligence presents new complexities for understanding and challenges for our field beyond the boundaries of what has been previously touted by many disciplines, including psychology. It is an opportunity to re-envision what it means to be intelligent in a diverse global context while also honoring and recognizing the value of difference, positionality, and other ways of knowing.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".