Timefulness: an Important Conceptualization but Needing a Better Approach
Bibliographic record
Abstract
I read Marcia Bjornerud’s Timefulness: How Thinking like a Geologist Can Help Save the World on the stair machine, as I do all my books for pleasure—it is the only time I have to do such reading, thanks to my self-imposed super busy schedule. My hope was that a book focused on thinking about time might help me improve how I schedule it, or at least help me to better appreciate how I spend it. Educated as a geologist and a science educator, I am a geology instructor at a university (as well as a recovering high school Earth science teacher). With this range of teaching experience, I am very familiar with students’ challenges in telling geological time, both in terms of relative dating and sequence of events, and in terms of the sheer magnitude of the age of the Earth and all that exists on it. As a concerned citizen of the Earth, I also realize that many of the problems that face humanity today are geological in nature (mineral and energy resource extraction, clean water, soil erosion, breathable air, climate change, and the various and sundry geologic hazards that threaten human life and property), and can only be solved (or at least mitigated) by understanding the phenomena involved. All this requires geological thinking.
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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.010 | 0.124 |
| Scholarly communication | 0.024 | 0.054 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".