Organizing a Rural Transformation: Contrasting Examples from the Industrialization of Tree Harvesting in North America
Bibliographic record
Abstract
"The transformation of work and the technological innovation that makes it possible usually doesn’t “just happen”. The greater the difficulty and cost of the tasks of innovation, the more likely successful innovation requires that innovation be deliberately organized and sustained. Few cases can be more illustra- tive of this than the industrialization of tree harvesting in North America after WWII. In this article we exa- mine the processes by which the harvesting of pulpwood in two contrasting regions of North America, both highly dependent on the pulp and paper industry, were transformed in the post WWII era. We establish that Eastern Canada was a leading region in the mechanization of woods work and the American Southeast a lag- gard. We delineate what it took to create and lead in a sustained industrial revolution in the woods in Eastern Canada, and that this did not happen in the American Southeast. We then suggest why the powerful business interests in Canadian forestry took strong measures to promote innovation, and why the same interests in the United States were able to avoid strong involvement in the transformation of woods work."
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".