Continuous Partial Attention Teaching and Learning in the Age of Interruption
Bibliographic record
Abstract
In the last quarter of the 20th century, the computer, and the seemingly endless repositories of data it generated, gave rise to what has been called The Age of Information. Today, the Internet and handheld devices offer the additional possibility of constant connectivity, which means that these technologies become sources of both endless information and perpetual distraction. For this reason, Thomas Friedman (2006) insists that we have moved from the Age of Information to the Age of Interruption. “All we do now, ” he says, “is interrupt each other or ourselves with instant messages, e-mail, spam or cellphone rings.” In the Age of Interruption, there is plenty of information, but attention has become a correspondingly scarce resource. “What the Net seems to be doing,” observed Nicholas Carr (2008) in a controversial Atlantic article, “is chipping away my capacity for concentration and contemplation ” (p. 57). A number of other recent books and articles speak to a similar concern: the technology that was once associated with intelligence, and a widespread optimism about its power to liberate the human mind, is increasingly portrayed as diminishing our capacity to pay attention, to stop and think.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".