Global environmental change: Economic and labour market implications for small island territories
Bibliographic record
Abstract
Rising sea levels threaten coastal communities and \ntrigger wholesale evacuations. Changing atmospheric \nconditions reduce rainfall and exacerbate \nflash \nfloods. \nOcean acidi fication leads to the collapse of sh stocks. \nSalt water intrusions prejudice water supplies and jeopardise crops. Most predictions of environmental change \nportend a signifi cant impact on island environments \nthroughout the world, including the extinction of endemic species and the wholesale depopulation of island \ncommunities (e.g. Tompkins et al., 2005). Stark impacts include the wholesale `drowning' or `disappearance' of such small island states as Kiribati, Tuvalu, \nthe Marshall Islands and the Maldives (e.g. Farbotko, \n2010). \nAlready susceptible to environmental impacts, and \nwith fragile economic systems, the world's numerous \nsmall island states and territories are likely to experience large-scale shifts in their economies and labour \nmarkets as a result of the impact of global environmental change. Given their geographical parameters, \nagriculture (including viticulture), fisheries, tourism and \ntransportation cut across most small island states and \nterritories as four critical economic and labour market \nsectors, deserving special research and policy attention. \nSo much is at stake. \nHow, then, does a policy maker, an industry investor, \nan employer or a trade union offi cial in a small jurisdiction like Malta make sense of the considerable data and \nscience about environmental change (including climate \nchange) in order to make smart decisions about future \ntrends and needs? How can we develop a better understanding of the implications of global environmental \nchange on tourism, air/sea transportation, agriculture \nand fi sheries in Malta? And how does this knowledge \nand methodology help develop a template that can also \nbe profit tably utilised in other small island states and \nterritories? \nTo attempt a tentative but legitimate answer to these \nburning questions, an international symposium was held \nat the Valletta Campus of the University of Malta from \nDecember 1{5, 2014 (CLS-IES, 2014). The event was \nbased on a collaborative eff ort between the Centre for \nLabour Studies and the Institute of Earth Systems, both \nat the University of Malta; along with the University \nof Prince Edward Island, Canada (through its Climate \nChange Lab); the University of the West Indies, Carib- \nbean; and the Smithsonian Conservation Biology Insti- \ntute, Washington DC, USA. This symposium brought \nto bear leading-edge environmental science not for its \nown sake, but in direct and specifi c application to the \neconomic and labour market predicament of Malta as \na small island state, facing the brunt of the impacts of \nglobal environmental change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".