Bibliographic record
Abstract
Shorelines are at the forefront when it comes to the effects of climate change. They are equally a preferred leisure destination for global northerners to seek respite and some sort of ecological reconnection. This article argue that the coast offers valuable insight as a literary site of disruptive encounters. At the coast, economic, ecological, and cultural disparity interweave, and can therefore carry manifold connotations, emotions, and prospects dependent of your vantage point. As such, I argue, the coastal site offers fundamentally different temporalities and experiences depending on the vantage point. To exemplify this point, the article examines contemporary registrations of the southernmost European shore in the wake of the so-called migrant crisis that occurred as the Arab Spring revolutions was met by autocratic pushbacks. Furthermore, the article presents the term ‘coastal world literature’ as a methodology of interpreting literature at the dynamic littoral zone between land and sea. Readings of the novel What Strange Paradise (2021) by Egyptian-Canadian author Omar El Akkad, the collection of poems Mare Nostrum (2019) by Libyan-American author Khaled Mattawa , the novel Til stranden (2017; To the Beach) by Danish author Peter Højrup, and the collection of poems titled Bag bakkerne, kysten (2017; Behind the Dunes, the Coast) by Danish author Peter Clement-Woetmann support the assertion that coastal texts are informed by their position within the world-system. In effect, coastal world literature reveals valuable first encounters of disparity, unevenness, and the range of accompanied affective responses. Consequently, what happens at the shore and how we tell it matters immensely.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".