Bibliographic record
Abstract
In the 10 years since the first issue of Gender, Place and Culture was published, feminist geography has grown, matured, become part of the normal curriculum in most departments of geography. The need to consider gender as a fundamental aspect of social life has become accepted wisdom. We have much to celebrate. Over the same period, increasing attention has been paid to questions of racialisation, and to projects that set anti-racism on the academic agenda. While I would argue that, socially as well as academically, we have made more progress in overcoming gender barriers than racial barriers, a growing body of work recognises the intersection, indeed the simultaneity, of sexism and racism, as well as classism, ableism and homophobia. Such recognition has characterised the pages of Gender, Place and Culture from its very first issue. Indeed, no paper that addresses issues of social exclusion from a geographical perspective would fail nowadays to make several references to articles in this journal. Theoretically, the connection between gendered and racialised social constructions heightens social awareness of the ways in which social exclusion occurs. It is now received wisdom, well beyond the narrower confines of feminist and anti-racist scholarship, that human attributes are the result of social construction and, while many controversies rage over the findings—and the social effects—of the postmodern ‘turn’, this fundamental theoretical tenet is hardly questioned by intellectuals of the early twenty-first century. Broader attention has now been focused on issues of what kind of society—and what kind of theoretical underpinnings—will replace a world in which social constructions such as gender and ‘race’ are taken for granted. Perhaps the most significant general trend of the last decade, then, has been the fact that our journal has played such an active role in the transition from the early 1990s' struggle to overcome essential ideas to today's struggle to re-place essential ideas with a new geometry of human relations. Significant historical events on every social front emphasise the difficulties of that transition, both theoretically and empirically.
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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".