Railway fuel and its impact on the forests in colonial India: The case of the Punjab, 1860–1884
Bibliographic record
Abstract
Abstract Recent studies have stressed the need for micro-histories of the environment so that important differences and similarities at local, regional and national level might be revealed. This paper analyses the process and patterns of environmental degradation at regional level by taking the case of deforestation in colonial Punjab by studying its implication at the level of empire. More specifically, it examines three aspects of how the operation and expansion of railways from 1869 to 1884, a peak period of railway expansion, affected the forests of the Punjab's plains. First, the paper analyses the reasons for large-scale railway expansion in the Punjab by discussing spatial and temporal expansion. Secondly, the impact of the railway firewood demand on the Punjab's forests between 1860 and 1884 is examined, specifically, the conditions that facilitated the increased dependence of the railways on firewood. Next follows an examination of the temporally varying nature of deforestation, given that railway firewood demand was determined by railway line openings. This section also includes a discussion on the nature of the colonial state response to the deforestation crisis and its role in maintaining the fuel supply to the railways. Finally, in the context of deforestation in the Punjab, the paper discusses how and why railway fuel changed from firewood to coal.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".