Book Review: Learning in a Time of Abundance: Learning in a Time of Abundance: The Community is the Curriculum
Bibliographic record
Abstract
Learning in a Time of Abundance: The Community is the Curriculum reviewed by Managing the abundance of information-or bleaker still, content-in our lives is an ongoing challenge for us all.It is certainly a classroom issue: Does it make sense to teach content and insist on rote memorization in a world where students carry all the information of the whole discipline on a device in their pockets?But it is also a problem we all face outside of the classroom: How do we manage partisan political messaging, health misinformation, and biased news as it floods out of the same devices in our own pockets?Enter Dave Cormier, who shares in Learning in a Time of Abundance: The Community is the Curriculum his prescription for beginning to manage this load.He suggests the importance of three literacies for the twenty-first century: humility, informed trust, and the ability to apply values to decision making as the place where welearners, teachers, and everyone-must start.Humility is the choice not to weigh in on matters that we don't really know about; to only contribute when our contribution makes things better.Informed trust is about checking the sources that inform our thinking and sharing those sources openly.And applying our values means interrogating and being aware of the values that underpin our practice now, and being willing to change those practices that don't serve our core values.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.035 |
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".