© 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".