Four years of sharing teaching practices within the French Computer Graphics community
Bibliographic record
Abstract
This paper describes and provides feedback on a Computer Graphics (CG) teaching initiative conducted by the French Association of CG (AFIG in French), as part of its annual national conference (called j.FIG). The AFIG, historically focused primarily on research and doctoral education, has been leading the French academic community in CG for 30 years. Since the beginning of 2021, it has launched a working group dedicated to CG teaching in the Bachelor's and Master's cycles. Its main action was to present panels during the j.FIG, to address issues related to CG teaching on a national scale. This is analyzed in detail in this paper. For each of the four organized panels so far, we present its main goals and the underlying discussions and repercussions, by comparing them with similar state-of-the-art initiatives. Possible actions and proposals to perpetuate the event are finally discussed. More broadly, our ambition is to obtain reactions and provoke necessarily enriching discussions, enabling everyone to escape a little from the teacher's solitude, alone in front of his class.
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".