EDUCAUSE 2022 French Delegation Report - EN: Visits to Colorado University of Boulder, Colorado Community College System, Community College of Denver & Internet2
Bibliographic record
Abstract
For the ninth consecutive year, a French Delegation has been formed to attend the annual EDUCAUSEconference. This initiative, which has been underway since 2013, brings together complementary profiles fromthe Digital world of French Higher Education community. We hope that this opening will provide sources ofinspiration and points of comparison, as well as vectors for promoting existing or upcoming projects.Thus, from an initial philosophy of simple participation that prevailed during the first years of this Delegation, wehave progressively moved to a more active logic, at various levels. More than 15 presentations accepted for thedifferent EDUCAUSE conferences on several academic topics have thus been given by members of our groupsince 2016. In addition, members of the Delegation's steering committee are actively involved in the life ofEDUCAUSE as members of the panel of experts gathered for the EDUCAUSE Horizon Report, as members ofseveral Community Groups (XR and Learning Spaces in particular), or as writers of reference articles andtranslators of tools. This involvement also reflects itself in the organization of the EDUCAUSE AnnualConferences and EDUCAUSE Learning Initiative Annual Meetings, for which we are not only mobilized each yearas proposal reviewers, but also as members of the respective program committees (in 2019 and 2023).The recognition of these different levels of involvement has allowed us to build a privileged relationship withEDUCAUSE and in particular its executive members (President, Vice-Presidents and Directors). Our actionshave also given us significant visibility in the North American academic community and beyond, which has led tothe settlement of formal collaborations, some of which are long term. In particular, various cooperations arebeing established with the EDUCAUSE Japanese Delegation.These different approaches contribute to reach our fundamental goals: exchange, inform, and share in order tosupport the development of digital in Higher Education.Our traditional restitution is a concrete expression of this desire, and has become a major annual event, whichwe know being appreciated. As for the accompanying report, it has an ever-increasing international audience,which is supported in particular by an English translation that we have quickly put into effect. Outside of Europe,it is awaited and read every year in the United States, Canada, Japan, Singapore, Australia and New Zealand.This new edition of the report follows the restitution held in Paris on January 27, 2023. It covers the differentworkshops followed during the EDUCAUSE conference held in October 2022 in Denver, as well as the four visitsthat the return to face-to-face meetings allowed us to organize again. It can also be completed by the differenttweets posted by the Delegation under the #EDU22fr hashtag.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.283 | 0.074 |
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".