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Record W2312043369

Kommunikasie tussen staat en burgers : die stand van tolkdienste

2006· article· nl· W2312043369 on OpenAlexaboutno aff
Marné Pienaar

Bibliographic record

VenueJournal for Language Teaching · 2006
Typearticle
Languagenl
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInterpreterLegislatureContext (archaeology)Political scienceState (computer science)ConfidentialityImmigrationPerceptionIdentity (music)Public relationsSociologyPsychologyLawGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates the dominant use of English by institutional service providers such as courts, state hospitals and pharmacies, state departments and local governments, South African provincial legislatures and tertiary institutions of learning. It is argued that in contrast to other countries, i.e. Belgium, Canada and Sweden where interpreting services are primarily used to accommodate immigrants, South Africa requires interpreters to communicate with its own citizens. The absence of interpreting services makes for an alarming level of misunderstanding and disempowerment affecting the quality of services provided by institutional service providers and barring citizens from information and help. However, when trained interpreters (liaison and conference interpreters) are available, a lack of insight into their role as well as a perceived breach of confidentiality, often result in frustration for all parties concerned. This paper will attempt to give a broad overview of the current state of interpreting in South Africa with specific reference to the use of interpreting services to facilitate communication between state and parastatal institutions and citizens. In conclusion, the impact of a lack of language facilitation, and in a South African context, the hegemony of English, on the individual's perception of his/her identity is considered.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.427
Teacher spread0.394 · 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 designQualitative
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

Citations5
Published2006
Admission routes1
Has abstractyes

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