Bibliographic record
Abstract
The following paper discusses and reflects upon the practices of organizing and attending the 2022 hybrid International Feminist Geography Conference (FEMGEOG), held in a range of physical and virtual locations and settings and across time zones. Through a series of short reflections written by members of the organizing committee it considers the practices of conference planning and organization alongside people’s experiences of their involvement and participation at different stages of the endeavour. The title of the conference, Pushing Boundaries, represents how the ethos and organisation of the conference sought to push against pandemic related travel restrictions, academic hierarchies of knowledge production and the financial inequities of conference attendance. It aimed to foster new means of connection, community and ways of relating to each other, and our research, through its international, multi-hub format. The paper also discusses the tensions and difficulties of organising an event of this kind, with reference to workload, funding and the technological demands and competencies required to foster inclusivity and connection. These reflections work as a means to provide advice and support for feminist geographers in the development of similar, future events.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 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".