Bibliographic record
Abstract
As the world continues to address the global COVID-19 pandemic and the many scientific challenges that it brings, teachers were relatively united when it came to identifying the biggest challenges that students might face in their future. Around a quarter of all respondents cited climate change, reflecting the pervasiveness of the issue throughout the tumult of the past year or so and the relevance of their subject to addressing the threat to the natural environment. Interestingly, fake news was the second most widely cited challenge that students are likely to face in their future. This issue is gaining extra attention in response to the COVID-19 pandemic. Respondents felt that it has never been easier for individuals to spread fake news, and never harder to distinguish what is a scientific fact from what is fake. Critical thinking is a key skill for learners in this environment. The pace of change, both in terms of technological advancement as well as more general societal change, was also felt to be a significant challenge that students will have to face. Technology was a common thread throughout the responses to this question, citing data analysis, the fourth industrial revolution, AI, and technology in the workforce as future challenges.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".