MétaCan
Menu
Back to cohort
Record W2964239723 · doi:10.1071/rj18105

Australian rangeland futures: time now for systemic responses to interconnected challenges

2019· article· en· W2964239723 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Rangeland Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsKensington Health
Fundersnot available
KeywordsRangelandLivelihoodRangeland managementIndigenousEnvironmental resource managementCorporate governanceBusinessPopulationEnvironmental planningNatural resource economicsAgroforestryGeographyEcologyAgricultureEconomicsFinanceSociology

Abstract

fetched live from OpenAlex

Australia’s rangelands contain wildlands, relatively intact biodiversity, widespread Indigenous cultures, pastoral and mining industries all set in past and present events and mythologies. The nature of risks and threats to these rangelands is increasingly global and systemic. Future policy frameworks must acknowledge this and act accordingly. We collate current key information on land tenures and land uses, people and domestic livestock in Australian rangelands, and discuss five perspectives on how the rangelands are changing that should inform the development of integrated policy: climate and environmental change, the southern rangelands, the northern rangelands, Indigenous Australia, and governance and management. From these perspectives we argue that more attention must be paid to: ensuring a social licence to operate across a range of uses, acknowledging and supporting a younger, more Indigenous population, implementing positive aspects of technological innovation, halting capital and governance leakages, and building human capacity. A recommended set of systemic responses should therefore (i) address governance issues consistently and comprehensively, (ii) ensure that new technologies can foster the delivery of sustainable livelihoods, and (iii) focus capacity building on a community of industries where knowledge is built for the long-term, and do all three of these with an eye to the changing demographics of the rangelands.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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