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Record W4396217155 · doi:10.19088/clear.2024.002

Innovations and Challenges in Crisis Contexts: Bangladesh’s Social Protection Response to the Covid-19 Pandemic

2024· report· en· W4396217155 on OpenAlexfundno aff
Kate Pruce

Bibliographic record

Venuenot available
Typereport
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersInternational Labour OrganizationForeign, Commonwealth and Development OfficeMinistry of Health and Family WelfareInstitute of Population and Public HealthInternational Fine Particle Research InstituteGovernment of the United KingdomDepartment of Social Services, Australian GovernmentUNICEF
KeywordsSocial protectionPandemicCoronavirus disease 2019 (COVID-19)Political scienceAccountabilityPublic relationsGovernment (linguistics)Stigma (botany)Economic growthCash transfersLegitimacyBusinessDevelopment economicsPovertyEconomicsPsychologyMedicinePolitics

Abstract

fetched live from OpenAlex

Many countries around the world introduced new social protection measures in response to the Covid-19 pandemic. In Bangladesh, innovations included emergency schemes such as cash support for informal workers and digital methods of delivery. However, for many people, the government safety net packages were unavailable to them, or they were ashamed to make a claim, or it was not enough to meet their basic needs. This Covid-19 Learning, Evidence and Research Programme (CLEAR) Synthesis Paper explores three emerging themes: (1) targeting and access to social protection, (2) legitimacy and accountability, and (3) the role of stress, stigma, and social norms. It then discusses these themes in relation to global debates about social protection and identifies key areas for further research to improve future social protection targeting and delivery in Bangladesh.

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)0.010
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.010
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.402
GPT teacher head0.501
Teacher spread0.099 · 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
Published2024
Admission routes1
Has abstractyes

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