That awful secret of the wood' : the forest and the EcoGothic
Bibliographic record
Abstract
When we imagine the forest, we tend towards extremes. It is commonly read as a binary space: as either ‘good’ or ‘bad’. When it is ‘good’, it is a remedial setting of wonder and enchantment; when it is ‘bad’, it is a dangerous and terrifying wilderness. It is with its fearsome associations that this thesis is concerned. Sarah Maitland, in her book Gossip From the Forest (2012), argues that ‘inside most of post-enlightenment and would-be rational adults, there is a child who is terrified by the wild wood’.1 The implication in her wording is that the modern adult who fears the forest does so despite the fact that he or she is ‘post-enlightenment’ and ‘would-be rational’. It is suggested, therefore, that such fears are today not only unfounded, but regressive and irrational. Nonetheless, as she continues, there is much evidence to suggest that we continue to be ‘terrified by the wild wood’. Popular culture abounds with seemingly infinite examples of the foreboding forest. It is, as a site of trial, trepidation, and terror, one of the most enduring and pervasive in our fictions. The central question of this thesis, therefore, is why do we continue to find this landscape so frightening? This thesis seeks to answer this question by examining a range of twentieth and twenty-first century Gothic texts, each of which features a fearsome forest.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".