Wood Pulp and the Emergence of a New Industrial Landscape in Maine, 1880 To 1930
Bibliographic record
Abstract
Between the 1880s and 1930s, investors developed over seventy pulp and paper mill sites to exploit the woods and inland waters of Maine. Authors John Clark and Deryck Holdsworth tracked the changing historical geographies of papermaking in Maine during this period through an analysis of data from Lockwood’s Directory, the industry’s leading monitor of investment. They also mapped mill sites, noting their changing capacity and shifts in product types as consumer needs evolved. Their work shows how the development of a railroad network helped facilitate a shift from smaller mills at coastal sites to larger mills at inland settings, which exploited water power from the state’s major rivers. This spatial shift, they argue, was also accompanied by an increasing portion of the ownership being controlled by out[1]of-state capital. John Clark, Data Visualization and GIS Librarian at Lafayette College in Easton, Pennsylvania, is a contributing author to the Historical Atlas of Maine (2015). Deryck Holdsworth, Emeritus Professor of Geography at Pennsylvania State University, is the co-editor of the Historical Atlas of Canada, Vol. III: Addressing the Twentieth Century (1990). The authors would like to thank an anonymous reviewer as well as Professors Stephen Hornsby and Anne Knowles of the University of Maine for their careful reading and insightful critique of this paper
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 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".