Bibliographic record
Abstract
At our research centre we have employed a hermeneutic phenomenological approach within a broad spectrum of projects to help us to better understand everyday human experience for the people for whom we wish to design. We have experimented with and explored creative ways to 'enter into' the lives of individuals and groups within diverse industry sectors. Finding new ways to capture lived experiences; understanding hidden 'meaning structures' within them and communicating these insights experientially are the goals driving this work. In this paper we share some examples of how we achieved these goals by infusing design thinking with hermeneutic phenomenology across four stages of our projects - Exploring; Sharing; Understanding and Showing How. These stages are kept rigorous by constantly referring back to philosophical first principles to inspire new techniques and 'ways into' the life-worlds of real people. We hope that designers and engineers will find these examples helpful in their attempts to find new perspectives on old problems and to challenge old perspectives on new problems.
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 | 0.114 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.080 |
| Scholarly communication | 0.031 | 0.048 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".