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Record W7032995351

Positive and negative effects of CSR : the case of disabled workers integration in the portuguese context

2020· dissertation· en· W7032995351 on OpenAlexaff

Bibliographic record

VenueRepositório Institucional da Universidade Católica Portuguesa (Universidade Católica Portuguesa) · 2020
Typedissertation
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsImpact
Fundersnot available
KeywordsNucleofectionHyporeflexiaTSG101Circumstantial evidenceDemotionTubulopathy
DOInot available

Abstract

fetched live from OpenAlex

Corporate Social Responsibility ability to respond to environmental and social problems is beneficial in different ways. The integration of People with Disability (PwD) in the workforce is one expression of CSR that can have important positive impacts. However, little is known about the specific impacts of the colleagues responsible for the integration and supervision of PwD. This is the major objective of this research. Two studies, based on a survey and individual interviews, provide some initial evidence on this matter. We found that in Portugal the inclusion of PwD is considered positive. Thought, it is a process held informally my companies, where stigmas and mistrust concerning the ability of these individuals to perform the job, can still be found. The results also show that experience, sensibilization, and preparation are the key to promote better and impactful integration. The insufficient awareness regarding disability suggests a collective and global effort, led by governments and organizations, that should alert and incentivize a whole nation to break this social barrier and turn it into opportunities. Well-structured and implemented inclusive programs can easily create win-win situations. By promoting all their employees’ engagement and potential, organizations will enhance individual and collective results, levering its differentiation, and competitiveness. We argue that the inclusion of PwD is an HR initiative valuable for any organization and indispensable for social progress.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.241
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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