Bibliographic record
Abstract
A threat to nocturnal ecosystems and human health alike, light pollution is an unnecessary problem that comes at an enormous cost. The International Dark-Sky Association has recently estimated that the energy expended on light scatter alone is responsible for no less than twelve million tons of carbon dioxide and costs municipal governments at least $1 billion annually (“Economic Issues” 2). Emerging research also suggests that excessive artificial light at night may compromise melatonin production, a hormone that has been linked to the suppression of certain cancers (Stevens 28; Haim 32). As scotobiologists seek to solidify the connection between the disruption of circadian rhythms and compromised states of physical and mental well-being, the impetus to study the cultural and literary meaning of the night sky becomes all the more pressing. Drawing on a range of affect theorists, the findings of nocturnal ecologists, and ecocriticism’s call to memory and mindfulness, this dissertation assembles a diverse crew to consider the ways in which Canadian writers have chronicled the shift from natural darkness to artificial light. Too easily dismissed as nostalgic or sentimental, the desire to see the night sky make its return has never mattered more. To live in a time and a place where night never fully arrives is to know that the stars in a given volume of poetry may well outnumber those that remain visible in the sky. Literature itself has now drifted into an era of post-darkness, the world’s obsession with artificial light having ushered in a historical period that is, quite literally, after dark. For this reason, stories and poems that are rich in celestial allusions are worth studying because they place personal reflection, cosmological awareness, and empathetic witness in a century that has otherwise failed to appreciate the necessity of nocturnal environments the world over. Favouring lyrical persistence over nocturnal lament, the Nova Scotian poet Kenneth Leslie once set sail to “stubborn stars,” his imagination desiring those truths that only take shape in a sea of dark (1). Seventy-five years later, his finest sonnet still invites us to follow – headlong into the passages we find in search of better light to read by.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".