Proceedings of the 2010 ACM conference on Information technology education
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2010 ACM Conference on Information Technology Education -- SIGITE 2010. This year we continue the SIGITE tradition of the conference being hosted by a regional IT leader - Central Michigan University in Midland, Michigan. Our organization has grown to become the premier forum for the presentation of pedagogical, theoretical and experiential issues related to IT education. Our mission is to share new knowledge and experiences, and to identify new directions for future research in IT education. Since 2000, the group that has become SIGITE has given educators, researchers and practitioners an opportunity to share their perspectives with others interested in IT education. The call for papers attracted more than 70 abstract and full paper submissions from Asia, Canada, Europe, and the United States. The program committee accepted 34 papers and posters that cover the five pillars of IT including Web systems, programming, networking, information management and human-computer interaction. In addition, the program includes a panel on engaging students in information storage management courses, and this year, special sessions on IT research, accreditation issues, and developing two-year curricula for IT. We are also inaugurating a Best Paper award, which recognizes the author of the paper that was rated among the highest by peer reviewers, and then received the highest rating by a consensus of the SIGITE Leadership Committee. We congratulate Randy Connolly, of Mount Royal College, for his paper, Small service is true service while it lasts: integrating web services into IT education, which demonstrates excellence in its clarity, originality and potential contribution to our field.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.216 | 0.152 |
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".