Learning by Doing, Knowledge Spillovers, and Technological and Organizational Change in High-Altitude Mountaineering
Bibliographic record
Abstract
We present an analysis of microlevel data from mountaineering on the 14 peaks over 8,000 m in height during the period 1895-1998. Prior to 1950, no expedition was successful in making an ascent and almost half of expeditions experienced a death, frostbite, or altitude sickness. By the 1990s, however, over half of the expeditions would successfully make an ascent and only about one in seven would experience an adverse outcome. Our objective is to distinguish between the effects of learning by doing and knowledge spillovers versus the effects of changes in technology or economic organization in explaining these results. As we can identify each climber by name and nationality, as well as each expedition team's methods and outcomes, we are able to disentangle the effects of learning at the individual, national, and international levels from effects due to improvements in climbing technology or changes in organizational methods and objectives. We find evidence that both individual learning by doing and learning through knowledge spillovers have contributed to the observed increase in ascent rates and to the decrease in death, frostbite, and altitude sickness rates.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".