MétaCan
Menu
Back to cohort
Record W4293753906 · doi:10.1080/0015587x.2022.2044598

‘Something’s Not Right in Silverhöjd’: Nordic Supernatural and Environmental and Species Justice in <i>Jordskott</i>

2022· article· en· W4293753906 on OpenAlexfundno aff
Heidi Kosonen, Pauline Greenhill

Bibliographic record

VenueFolklore · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarmEconomic JusticeCreaturesAmbivalenceSociologySociocultural evolutionEnvironmental ethicsNorwegianCriminologyIntervention (counseling)Political scienceGeographyAnthropologyPsychologyNatural (archaeology)LawSocial psychologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

In the Swedish/Finnish/British/Norwegian television series Jordskott (2015–17) child victims’ mysterious disappearances signal that ‘something’s not right’ in Silverhöjd, a Swedish town. Three detectives uncover a conflict between the locals who depend on a local industry and preternatural human-like but non-human forest creatures familiar from Nordic tradition and fairylore. Both humans and semi/non-humans are ambivalent, but what sets the latter apart is their implication in caring for nature, protecting it, and punishing those who harm it. We analyse this series’ instantiation of a folkloristic popular green criminology, based in the idea that popular discourses’ representations of crimes and harms may offer serious interventions into issues of environmental, ecological, and species justice. Our ecocritical analysis suggests that tradition offers material for reflection, but also for sociocultural intervention, as it is circulated to address the dependencies and hierarchical configurations between humans and other life-forms, and the many consequences of ecological and individual harm.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.025
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.269
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFolkloreSame topicGeographies of human-animal interactionsFrench-language works237,207