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Record W2058060069 · doi:10.5402/2012/419782

Ecosystems, Pollution, and Use of Resources in Textbooks of 14 Countries: An Ecocentric Emphasis

2012· article· en· W2058060069 on OpenAlexfundno aff
Rosa Branca Tracana, Graça Simões de Carvalho

Bibliographic record

VenueISRN Education · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInternational Council for Canadian Studies
KeywordsAnthropocentrismContext (archaeology)Position (finance)LimitingPerspective (graphical)Sustainable developmentResource (disambiguation)Environmental ethicsEnvironmental educationRelation (database)Environmental pollutionSociologyPolitical scienceEngineering ethicsEnvironmental resource managementEnvironmental protectionGeographyPedagogyBusinessLawEnvironmental scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Two views of Human-Nature relation can be found: anthropocentrism and ecocentrism. In order to understand how school textbooks refer to the human’s position in nature we analysed how “Human as guest versus Humans as owners of nature” is present in the three topics of environmental education— Ecosystems, Pollution , and Use of Resource —in textbooks of 14 countries from Europe, Africa, and Middle East. A specific grid of analysis, which was constructed in the context of the European Project BIOHEAD-CITIZEN, was used in this study. Results show that this axis of analysis is present in the majority of textbooks addressing the above three topics but not in the “ Biodiversity ” topic. Textbooks for 12–15-year old pupils were the ones having more occurrences than those for 6–11- or 16–18-year olds. The textbooks present mainly an ecocentric position, whereas the aesthetic, ethical, and cultural aspects are limited and inadequate, limiting the full perspective of education for sustainable development.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2012
Admission routes1
Has abstractyes

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