Honors International Faculty Learning Online (HIFLO 2020): A model for honors online professional development
Bibliographic record
Abstract
The Spring of 2020 brought many disruptions to our professional and personal lives due to the COVID-19 pandemic that forced worldwide mid-semester campus closures; pivoting of traditional, face-to-face classes to remote teaching and learning; and postponements or cancellations of conferences, workshops, and other professional development events. One example of the breakdown of scheduled opportunities for us as honors colleagues to gather in-person to enhance our practices and strengthen our community was the cancellation of the 2020 International Conference on Talent Development and Honors Education in Groningen, the Netherlands, originally slated for June 10-12 but moved to June 16-18, 2021. Immediately following the 2020 conference, we (the authors) had planned to offer the fifth Honors International Faculty Institute (HIFI), an international and highly interactive occasion for honors and talent development teachers, researchers, and leaders to engage in presentations, experiential activities, place-as-text explorations, collaborative group work, reflective exercises, and showcases designed to improve teaching, learning, and programming in honors. Suddenly, the coronavirus upended our world, and we had to reimagine the institute that we had previously organized four times alternately at Hanze University of Applied Sciences (Netherlands) and Texas Christian University (USA). Putting aside the disappointment of the moment and recognizing the value of coming up with an alternative to HIFI that would ensure the safety and health of our honors colleagues, we decided to create a fully online version with free registration to encourage participation and create resources accessible to all members of our international community. We wanted to highlight the challenges of how all of us unexpectedly had to pivot to remote teaching and learning as the global pandemic intensified, but we also wanted to share information, experiences, and models that could open new avenues for operationalizing online honors education more generally beyond the COVID-19 crisis. We wanted, in other words, to explore how honors pedagogy could (and maybe should) be adapted to the increasingly online world of primary, secondary, and higher education. Thus, HIFLO 2020 was born! HIFLO stands for Honours International Faculty Learning Online.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".