Part 1: Mapping the Landscapes: Introduction and Looking Backwards, Looking Forwards: Reflections
Bibliographic record
Abstract
Mapping the LandscapesThis anthology of evocative memories, stories/narratives, poetry, photographs and artwork, begins with a poem by Gloria Escoffery which questions our ways of knowing the natural and human world in which we live.The introduction creates the physical and conceptual maps which guide our collective eBook.To this end, candid reflective and reflexive essays by DePass, Lumsden, Browne, Cunningham-Heron, and Robertson-Hickling illustrate some of the key themes which are developed in different ways by the contributors.In the eBook, by speaking in our own voices, in our own ways, we highlight through stories/narratives and photographs, some of the impacts of learning Geography at the University of the West Indies (UWI).As importantly, we summarize lived experiences of formal, non-formal and informal learnings at the UWI Campus. SPRING Do you know why the sun shinesAnd the breeze throws Small seeds across the sky?Do you know why the seas heave And the young sing Small sounds without a sound?The universe spins, the world reels, and I See the street shining.Upside down You are steady-or do you spin too?
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".