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Record W4310651656

Infusion of indigenous knowledge into the teaching of ecotourism entrepreneurship.

2019· article· en· W4310651656 on OpenAlexaboutno aff
Dumsile Cynthia Hlengwa

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismEntrepreneurshipIndigenousTraditional knowledgeBusinessGeographyTourismArchaeologyEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

The discourse on homegrown knowledge has provoked a debate of larger-than-life proportions across the world over many years since the demise of colonialism. In Africa, especially in the sub-Saharan region, while the purportedly indigenous communities have always found worth in their own local forms of knowledge, the colonial administrations viewed indigenous knowledge as being unempirical, irrational, anti-developmental, and unchristian. The standing and status of indigenous knowledge has been transformed since the1997 Global Knowledge Conference in Toronto, which emphasised the pressing need to learn, preserve, and exchange indigenous knowledge. Student protests have been proliferating across South African universities since 2015 with students calling for free, quality, diverse and transformative decolonised education based on inter alia, indigenous African knowledge systems. This movement is not peculiar to Africa. It has reverberated to other parts of the globe such as New Zealand, England, Scotland, the USA and others, where students demand the removal of ‘dead white men’ from their curricula and call for the incorporation of indigenous and postcolonial thoughts and knowledge bases. This is not to say that best practices in curriculum creation from other parts of the globe should be ignored in entirety, but rather that African knowledge should be afforded its needed space. While the movement was initiated by academics decades ago, the actual transformation has been slow, because the minds of academics and physical spaces were conquered by the myths of superiority of whiteness and defeatist attempts of trying to dismantle the colonisers’ structures using the same tools colonialists used to build the current structures. While the students are not sure what decolonisation of the curricula would entail, they know that they want universities to be relevant spaces of equal opportunities where they do not feel alienated from themselves and what is African in essence. The design used in this study was a cross-sectional case study where an isangoma (traditional healer who uses long-established methods passed down from one healer to another to treat an ailing person suffering from various illnesses, some of which have a psychological basis) conducted a lecture on her trade as an ancestral gift and source of livelihood. This lecture demonstrated that education and indigenous knowledge (IK) can indeed coexist in a modern and technological harmonious lecture room. A group of 118 second year ecotourism students attended the lecture and then completed a questionnaire soliciting their thoughts about the lecture and the infusion of IK into their curriculum. The study found that while the majority of them were skeptical at the beginning, they felt that they learnt a great deal about ubungoma (traditional healing) as a gift from the ancestors and that the lecture had cleared misconceptions and made them proud of their southern African culture.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.107
GPT teacher head0.461
Teacher spread0.353 · 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 designNot applicable
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

Citations3
Published2019
Admission routes1
Has abstractyes

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