© The Author(s) 2015. This article is published with open access at Springerlink.com Eye opener: exploring complexity using rich pictures
Bibliographic record
Abstract
time ago-probably when we were still children. That was certainly the case for me until recently. Ten years ago I came to Canada to pursue my PhD. My first day at school was nothing close to what I was anticipat-ing. It was definitely exciting to feel the atmosphere of a research-intensive North American university while walk-ing through the beautiful campus. However, there were clouds, very dark clouds that suffocated me right from the first day at school: not being able to communicate as effec-tively as I used to in my own language, feeling so far away from home, feeling academically lacking and socially awk-ward. One day, I found myself drawing about these expe-riences (Fig. 1), and as I drew I realized the impact those clouds had had on my academic performance and profes-sional identity. I found myself wondering about how other
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.703 | 0.624 |
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".