Bibliographic record
Abstract
There is no escape from agriculture except into mass starvation, and it has often led there anyway, with drought and blight. Ronald Wright , A Short History of Progress Our early hunter-gatherer ancestors lived in isolated territories. One might think that, over time, their expanding populations would have exhausted resources in their territories and led them to develop agriculture, but the archaeological evidence does not support this view. Instead, hunter-gatherers practiced birth control by delaying the weaning of their children or, during the worst of times, killing the children or migrating elsewhere. Today there is ample evidence of farming societies encroaching on the territories of hunter-gatherers and compelling them to switch to farming. There are many cases where traditional hunting and gathering grounds have been wiped out by deforestation for lumber, agriculture, or access to mineral resources. Yet these circumstances did not apply when agriculture first came into being. Life also seems to have been a lot more leisurely for small groups of hunter-gatherers than for farmers , who had to work long hours to gain the same nutritional benefi ts. So making the transition to agriculture did not happen because it was less work or more fun. When resources became limited in their territories, hunter-gatherers just moved to more suitable environments, something quite possible when populations were small. There was no apparent time or energy benefi t that would have encouraged them to switch to agriculture. What triggered the shift?
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".