Bibliographic record
Abstract
In 2015, I published a piece of research on how potential charity donors see homeless people (Dean, 2015). Utilising a creative visual method of drawing, I gave participants pens and paper and asked them to ‘draw what homelessness looks like’. Their images overwhelmingly showed dishevelled men, sleeping rough or begging on the streets: stereotypical depictions of scenes of homelessness which we know represent only a small element of the huge and growing problem that is homelessness in the UK and around the world. The project aimed to explore the common perceptions of the problem of homelessness in the minds of potential donors, stemming from other research (Breeze and Dean, 2012, 2013) exploring the representation of homeless people in fundraising literature. I like to think of it as a simple and yet interesting project, where a creative method was justified in exploring a problem which would not work through purely verbal or textual methods. But the story of where the idea came from does say something about the individuality of researchers and the randomness of doing research. I presented our initial research findings on fundraising literature at a large non-profit and voluntary sector research conference in Toronto in 2011. While there, I got very annoyed at what I felt were an unbelievably boring set of oddly repetitive and characterless conference presentations. To compensate for this (and because I was an over-privileged, know-it-all British PhD student), I got a bit drunk and complained loudly about the rather lousy conference to someone I’d just met, who must have thought I was rather terrible.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.055 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".