Proceedings of the 2011 conference on Information technology education
Bibliographic record
Abstract
Welcome to West Point! It is our great pleasure to host the 2011 ACM Conference on Information Technology Education. The theme of this year's conference is Growth and New Vistas. The academic field of IT is experiencing many changes. As we enter a new decade in the Digital Age, IT will continue to face new opportunities, changes, and challenges. How do we continue to grow the profession and encourage more students to pursue IT? What is the scope of research for IT? What are the best practices for covering the knowledge areas in the IT Model Curriculum? How do we balance the challenges of accreditation against the need for curriculum evolution? Over the years, we have found this conference to be both a well spring of ideas to address issues such as the above and a much needed opportunity to network and bond with fellow IT educators. We hope you find this year's conference as beneficial and rewarding as the many we have attended in the past. We are truly fortunate this year to have Dave Ferrucci as our keynote speaker. Dave was a lead designer for the IBM Watson design team. We have heard him speak before, and we look forward to an exciting presentation. Other conference social activities include a unique tour of the West Point's cadet area and a beautiful and collegial boat ride on the scenic Hudson River. The call for papers attracted 95 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 49 papers, 13 posters, and 5 panels. This year the SIGITE reviewer pool grew substantially and we had over 90 reviewers and thus each submission was evaluated by at least four reviewers. The program committee would also like to thank those dedicated emergency reviewers who stepped in at the last moment to review one or two extra papers under tight time constraints.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.306 | 0.217 |
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".