Welcome to the fall 2020 issue of Intersection: A Journal at the Intersection of Assessment and Learning.
Bibliographic record
Abstract
Articles in this Issue: • Issue Overview: Persevering During a Pandemic The Resilience of Assessment Professionals During Challenging Times by Giovanna Badia • A Workshop on Online Exams as a Supportive Response to Faculty’s Needs During a Pandemic by Suzanne R. Horwitz and Mary English • Anyone Else Tired of Pivoting? Unmasking Student Learning and Community Assessment Needs by Tara K. Ising and James D. Breslin • Bettering Assessment Practices through Reflection and Collaboration by Carrie R. Allen • Considerations for Meaningful Assessment During COVID-19 by Laura M. Harrison and Marc Scott • Coping with COVID 19: Lewis University College of Business Experience by George Klemic • Home Alone but Working as a Team: Virtual Collaboration for Student Learning by Kristina Ramirez Wilson, Lucy James, and Newman Chun Wai Wong • Innovative Approaches to Programmatic Assessment in an Era of Flux by Jared Androzzi and Megan Schramm-Possinger • Leading with Empathy and Learning to Flex by Eileen Soto and Jamie Smith • Let’s Connect: Maintaining and Strengthening Collaborative Relationships in a Remote Environment By Gina B. Polychronopoulos and Emilie Clucas Leaderman • Non-Academic Assessment in the Era of COVID-19: Utilizing Bolman and Deal’s Four Frames by Kadie Hayward Mullins • Nurturing Relationships Grounded in Assessment by María B. Serrano Abreau • Perseverance in Teacher Preparation and Certification Assessments During the COVID-19 Pandemic (or Not) by Rebecca Z. Grunzke • Quarantining High-Stakes Assessments in an Introductory Physics Class by Richard L Pearson III and Chad Rohrbacher • Restructuring an Assessment Leadership Institute During the 2020 Pandemic by Yao Zhang Hill, Monica Stitt-Bergh, Adrian Alarilla • Silver Linings from a Challenging Situation by Elise Demeter, Christine Robinson, Mitchel L. Cottenoir, Harriet Hobbs, and Karen E. Singer-Freeman • The Value of a Curriculum Map to Minimize Harm from Emergency Remote Instruction by Karen Singer-Freeman and Mitch Cottenoir • University-wide Assessment During Covid-19: An Opportunity for Innovation by Dena Pastor and Paula Love
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.223 | 0.146 |
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".