Designing Affiliative Objects: Investigating the Affiliations of Medical Identification Jewellery
Bibliographic record
Abstract
affiliation of medical identification jewellery with paramedics as the central user group. In doing so, we use Suchman’s notion of the affiliative object to reframe medical identification jewellery as a compound epistemic object with affiliations to paramedics in the province of Ontario, Canada. The paper begins by providing background including the methods used to assess the use of medical identification jewellery. There follows a section on how the findings from fieldwork were used to develop a first iteration of design recommendations. A compliancy table then appends discussion of key findings and design recommendations. Three design concepts were found to be particularly successful in focus groups of participant paramedics. These were modified and evaluated in response to the feedback obtained. One concept was ultimately rejected, while the other two underwent redesign. The two successful concepts were developed into high-fidelity prototypes. The design concepts presented here are observably original and not copies of previous designs. As affiliative objects, they aim to facilitate diagnostic work in emergency response. In doing so, they follow Lucy Suchman’s (2005: 381) injunction that “the constitution of objects is a strategic resource in the alignment of professional identities and organizational positionings.”
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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".