From Awareness to HCI Education: The CHI'2005 Workshop Papers Suite
Bibliographic record
Abstract
These four papers are a suite of articles presented at workshops (listed in the individual citations) held at the ACM CHI 2005 conference, April 2005. Saul Greenberg. (2005) HCI Graduate Education in a Traditional Compute Science Department. ACM CHI 2005 Workshop on Graduate Education in Human- Computer Interaction. Organized by Beaudouin-Lafon, M., Foley, J., Grudin, J., Hudson, S., Hollan, J., Olson, J. and Verplank, B. Gregor McEwan and Saul Greenberg. (2005) Community Bar: Designing for Awareness and Interaction. ACM CHI 2005 Workshop on Awareness systems: Known Results, Theory, Concepts and Future Challenges. Organized by Panos Markopoulos, de Ruyter, Boris, and Mackay, Wendy. Carman Neustaedter, Kathryn Elliot and Saul Greenberg. (2005) Understanding Interpersonal Awareness in the Home. ACM CHI 2005 Workshop on Awareness systems: Known Results, Theory, Concepts and Future Challenges. Organized by Panos Markopoulos, de Ruyter, Boris, and Mackay, Wendy. Anthony Tang and Saul Greenberg. (2005) Supporting Awareness in Mixed Presence Groupware. ACM CHI 2005 Workshop on Awareness systems: Known Results, Theory, Concepts and Future Challenges. Organized by Panos Markopoulos, de Ruyter, Boris, and Mackay, Wendy.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.026 |
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".