Bibliographic record
Abstract
Contents: Introduction: geography and emotion a emerging constellations, Mick Smith, Joyce Davidson, Laura Cameron and Liz Bondi Part I Remembering: Road kill: remembering what is left in our encounters with other animals, Mick Smith Mapping shades of grief and consolation in the historic landscape of St Patrick's Isle, Isle of Man, Avril Maddrell Historicizing emotion: the case of Freudian hysteria and Aristotelian 'purgation', R. Darren Gobert. Part II Understanding: Understanding the affective spaces of political performance, Nigel Thrift Environmental aesthetics, ecological action and social justice, Jennifer Foster Learning from spaces of play: recording emotional practices in High Arctic environmental sciences, Richard C. Powell. Part III Mourning: 'What we all long for': memory, trauma and emotional geographies, Anh Hua Ephemeral art: the art of being lost, Mary O'Neill 'To mourn': emotional geographies and natural histories of the Canadian Arctic, Emilie Cameron. Part IV Belonging: Telling tales: nostalgia, collective identity and an ex-mining village, Katy Bennett Death and bingo? The Royal Canadian Legion's unexpected spaces of emotion, Deborah Thien 'I love the goddamn river': masculinity, emotion and ethics of place, Cheryl Lousley. Part V Enchanting: Enchanting data: body, voice and tone in affective computing, Frances Dyson Judith Merrill moving in and out of this world: urban landscape encounters of a science fiction personality in the 60s and 70s, Dianne Newell and Jolene McCann One stone after the other: geopoetical considerations on stony ground, Alexandre Gillet The steppe, Alphonso Lingis Index.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".