Bibliographic record
Abstract
Something extraordinarily strange is going on in the computer industry. Despite the widespread availability of jobs, the challenging and exciting nature of the work, and the impressive earning potential of workers, the (industry is experiencing a massive decline in the number of new recruits. Universities and companies alike are starting to wonder: where did all the warm bodies go? Numbers from the US are showing declines upwards of 20%, and across North America it is not uncommon to find decreases in students applying to computer science post-secondary programmes that double that. It appears that a dearth of highly-trained professionals is looming, which is bound to negatively impact future advances in computer technology and ultimately threaten major economies. Given the current crisis, many groups are addressing the issue, with varying approaches and perspectives. The film "The Elephant in the Room: Who Will Take Care of the Code?", exposes the symptoms of this problem and examines some of the current solutions. In particular, this film profiles efforts at the University of Victoria in the CScIDE Computer Science Initiatives for Diversity and Equity research group and their experiements introducing programming concepts to children in grades two through seven.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".