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Record W4238021679 · doi:10.4324/9780203012512-20

Water supply and management

2002· book-chapter· en· W4238021679 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Geography · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEnvironmental science

Abstract

fetched live from OpenAlex

The process of environmental impact assessment (EIA) was introduced for the first time in the United States in 1969 under the National Environmental Policy Act for all major federal activities. Since then, there has been an everwidening acceptance, particularly by the industrialised nations of the world, of the view that environmental effects likely to be caused by a proposed development are material considerations within any planning decision-making process. The influence of the US federal measures led to the rapid incorporation of EIA into state and local statutes across that country and then by the government of Canada in 1973. Many other developed countries followed including, Australia at commonwealth level (1974), Japan (1984) and New Zealand (1991). Although a number of its member countries, such as France and Ireland, had embraced EIAs as early as 1976, followed by the Netherlands (1981), the Council of Environmental Ministers of the European Communities did not adopt a Directive on EIAs for certain types of development until 1985. Their implementation became mandatory in 1988 ( Montz and Dixon 1993 ; Sanchez 1993 ; Geraghty 1996 ). As for developing countries, while many of the 121 sovereign states that might be so categorised had, by the 1990s, at least considered EIA legislation, only nineteen had put in place the necessary administrative, institutional and procedural frameworks for the implementation of EIA systems, only six of which were successfully operational ( Ebisemiju 1993 ).

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.010
GPT teacher head0.203
Teacher spread0.192 · 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