Henrion & Parkin’s Systematic Design Methods
Bibliographic record
Abstract
This seminar brought together six speakers invited to map current research drawing on F H K Henrion’s archive and to consider the opportunities it offers for research collaboration. The invited speakers were: Sue Breakell (University of Brighton), Adrian Shaughnessy (Royal College of Art), Rob Banham (University of Reading), David Preston (University of the Arts London), David Cabianca (York University, Canada) and Patrick O’Shea (Kingston University). The afternoon’s sessions related to existing research projects which, while they may not focus solely on Henrion, have his work as a component, contextualizing and enriching the field of study. The first of these was David Preston, a graphic designer and an Associate Lecturer at Central St Martins, as well as a PhD candidate at the RCA. Looking at several of Henrion’s early corporate design schemes, including Pest Control of Cambridge and the Dutch airline KLM, David described some of the systematic techniques developed by Henrion and his partner Alan Parkin to analyse and manage the various design outputs and formats in which corporate identity schemes would be presented. He set these in the context of other influential writing on systematic design methods such as that of Bruce Archer.
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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 | 0.259 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".