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Record W2807989326

Gazing back: A feminist postcolonial lens on tourism in the townships of South Africa

2018· dissertation· en· W2807989326 on OpenAlexaboutno aff
Meghan Muldoon

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTourismLens (geology)Through-the-lens meteringGender studiesGeographyOptometrySociologyMedia studiesEngineeringArchaeologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Encountering poverty in tourism is a morally fraught experience. Growing numbers of tourists are desirous of exploring off-the-beaten path adventures and this invariably leads to encounters with the Other in increasingly far-flung and improbable locales. As countries of the Majority World – where the majority of the world’s poor live – continue to host ever-increasing numbers of tourist arrivals (UNWTO, 2017), the potential of tourism to play a role in the alleviation of poverty is an alluring prospect.
\nDespite its economic potential, the postcolonial nature of many touristic encounters in the Majority World, as well as the very tangible harm that some forms of tourism have brought to the world’s poor, have caused many critical scholars to question the assertion that tourism may bring net-benefits to people living in poverty. Further, colonialized discourses of the exoticized Other, circulated through tourism marketing and the popular media, create essentialized images that inform tourists’ interactions with tourism hosts while traveling in the global South.
\nGuided by a feminist postcolonial theoretical framework, the purpose of this thesis research was to learn about how hosts gaze back at the tourists that spend time in the townships of South Africa where they live. Constructed as racialized spaces of economic and geographic segregation during apartheid, townships in South Africa continue to be homogenously black or coloured spaces characterized by poor infrastructure, inadequate housing, and economic marginalization. Following the end of apartheid, however, townships have also come to be demarcated as spaces of resistance and courage, of historical significance and triumph over oppression. It is into these spaces that a growing number of tourists choose to venture, travelling the streets of the townships on foot, on bicycles, or in vans.
\nEmploying a photovoice methodology for the purposes of this study, I gave digital cameras to 14 men and women living in three black townships on the outskirts of Cape Town and asked them to take photographs of how tourism is and how tourism ought to be. Through the photographs that they chose to share, participants spoke about the economic and social benefits that encounters with tourism had brought to their lives. They also spoke to the complex ways in which tourism to the townships is embedded within existing structures of race, class, gender, and postcolonial aftermaths. Employing a Foucauldian approach to discourse analysis, I strove to understand how relationships of power, embedded within these structures, inform the ways in which township residents conceptualize and seek out encounters with tourism.
\nComplicating this narrative was my presence in the townships as a white/Canadian/tourist/researcher. The narratives that were shared with me were filtered through the lens of my embodied presence, and led me to explore my own situatedness and biases through a number of reflexive research practices. This thesis analyzes the ways in which relationships of power based in race, gender, mobilities, colonial narratives, and financial resources inform touristic encounters in the townships of Cape Town, South Africa. This work contributes to the field of critical tourism and leisure studies by advancing our understandings of how tourism is conceptualized as powerful in a multitude of ways by tourism hosts in a unique part of the Majority World.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.239
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2018
Admission routes1
Has abstractyes

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