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Record W3109668779 · doi:10.17863/cam.59601

Chapter 8 - The intensification of the agricultural landscape of the Maltese Archipelago (Temple landscapes Fragility, change and resilience of Holocene environments in the Maltese Islands)

2020· book-chapter· en· W3109668779 on OpenAlexfundno aff
Jeremy Bennett

Bibliographic record

VenueOAR@UM (University of Malta) · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
FundersMagdalene College, University of CambridgeQueen's University BelfastEuropean CommissionQueen's UniversityUniversity of Cambridge
KeywordsMalteseArchipelagoGeographyAgricultureForestryAgroforestryArchaeologyEnvironmental scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Bowen-Jones et al. (1961) in Malta: Background for Development furnish the reader with a prophetic edict which carries the threat of environmental catastrophe unless there is continual human investment. This book provides an unparalleled geographic assessment of the Maltese agricultural economy, in the years prior to independence, and still serves as a compendium of knowledge and terminology nearly sixty years later. The authors’ opening gambit recognizes the swell in national identity and the resulting desire to exert more control over the nation’s socioeconomic direction. However, their opening tonality also expresses an awareness of the influence of development and its potential threat moving into the future, thus directing the authors to the formation of a study that facilitated a greater understanding of the interplay between socio-economics and the landscapes of the Maltese Islands. [excerpt]

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.005

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 head0.026
GPT teacher head0.208
Teacher spread0.182 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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