EDUCAUSE 2021 French Delegation Report - EN
Bibliographic record
Abstract
For the eighth consecutive year, a French Delegation was formed to participate in the annual EDUCAUSEconference. Since 2013, this initiative has been structured around complementary profiles from the FrenchHigher Education digital sector, all of them being keen to open up to the international arena in order to findsources of inspiration and points of comparison, but also to highlight remarkable achievements. From an initialphilosophy of simple participation, which prevailed during the first years of this Delegation, we haveprogressively moved to a resolutely more active approach, at various levels.The members of the Delegation's steering committee actively participate in the life of EDUCAUSE, throughvarious involvements: as proposal reviewers for the EDUCAUSE Annual Conferences and the EDUCAUSELearning Initiative Annual Conferences, as members of the panel of experts mobilized for the drafting of theEDUCAUSE Horizon Report, or as members of various thematic Community Groups (XR and Learning Spacesin particular). In addition, there have been about 15 presentations made by members of the French Delegation atvarious EDUCAUSE conferences since 2017.The recognition of these different levels of intervention has allowed us to establish privileged connexions withEDUCAUSE and in particular its executive members (President, Vice Presidents and Directors), and moreglobally within the North American Higher Education community, with which some formal collaborations havealready been established over time. These connexions contribute to the realization of our fundamentalobjectives: exchange, sensitize, inform, and share in order to support the development of digital technology inFrench Higher Education.Every year, our traditional restitution gives concrete expression to this desire, and has become a major annualevent that we know to be appreciated. As for the accompanying report, it is being disseminated more and more,and is based on an English translation that we have quickly put in place. Outside of Europe, it is awaited andread every year in the United States, Canada, Japan, Singapore, Australia and New Zealand. This new edition ofour report follows the restitution that took place on March 17th, 2022. It deals with the different workshopsfollowed during the EDUCAUSE conference held in October 2021. It can also be completed by the differenttweets posted by the delegation under the #EDU21fr hashtag.The 2021 edition of the EDUCAUSE conference was again placed as in 2020 under the sign of the global healthcrisis. It was only accessible by distance for international participants like us without flights. However, a limitednumber of sessions outside the plenary sessions were filmed and broadcasted. The short duration (one month)of online viewing of these videos would have made it difficult to write the articles in this report. However, ourintervention with the EDUCAUSE organization allowed us to have access to some of the sessions outside of thedeadline and we would like to thank our contacts for this.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.183 | 0.101 |
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".