Alexis Black

Statistical learning describes a phenomenon where individuals can detect probabilistic patterns in their environment. In language acquisition, this mechanism has been shown to support learners’ induction of phonological categories (Cristia, 2018), words (Black & Bergmann, 2017), and even morphosyntactic rules (Finn & Hudson Kam, 2008). However, studies demonstrating this phenomenon are typically tightly constrained and artificial; when the laboratory conditions are slightly more complex, learning is surprisingly fragile. In this thesis, I explore the idea that these learning conditions are too artificial and fail because they are missing a key component of the natural language environment: variability. Speaker variability—the acoustic variability that occurs when different people produce the same sounds—has been shown to improve the acquisition of novel language structures (e.g., Graf Estes & Lew-Williams, 2015). I hypothesized that adding speaker variability to an otherwise artificial statistical learning task would enhance learners’ ability to detect underlying statistical patterns.Two experiments were designed to assess the extent of listeners’ sensitivity to underlying structure and word segmentation in a statistical learning paradigm. Participants heard one or multiple voices speaking with either English or non-English phonology when listening to the syllable stream. The syllable stream is constructed such that four trisyllabic pseudowords can be extracted based on the transitional probabilities between syllables. Previous work had shown that adult learners fail to extract words when the phonology is unfamiliar (Black, 2018). It was anticipated that speaker variability would enhance learning in this condition. The second of the two experiments was designed to address questions raised by Experiment 1; it implements adjustments to the stimuli and longer exposure to the syllable stream and limits the sample to only monolingual English speakers. Results from both experiments show that speaker variability may interfere with statistical learning, no matter if the phonology is familiar or unfamiliar. This evidence is consistent with the prediction that speaker variability introduces processing demands that compete with tracking underlying statistics. However, a lack of evidence that the control conditions meet the expected learning outcomes indicate the experimental paradigm likely produces unexpected confounds that warrant further testing.

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In this thesis, I consider ethnographic conversations I had during fieldwork in Fort McMurray and Edmonton, Alberta, Canada, in 2016 with two sets of workers: Albertan trades-workers employed in the oil sands (pipe-fitters, welders and boilermakers) and Filipino/a Temporary Foreign Workers (TFWs) employed in the local service sector (cooks, caregivers and kitchen helpers). I analyse these workers’ self-reflections on their own work routines as providing a sightline into the ways labour market processes and regulatory frameworks are manifest in and negotiated through their lives. I draw especially on the theories of Karl Polanyi and Karl Marx in my analysis. Through ethnography I also show how the labour market processes these thinkers analyse shape, and are shaped by, social differences they each tend to neglect (e.g. nationality, citizenship, migration status, race, ethnicity, gender), and which more recent post-colonial, feminist, and critical race theorists have emphasised. Hence from the Albertan context, I conceptualise how state-regulated labour markets re-fashion, and are re-fashioned by, the cultural identities of workers. I show how local labour market processes re-make and aggravate social differences between Albertan trades-workers and Filipino/a TFWs in Alberta, in ways that are not superficially or simply motivated by forms of discrimination (e.g. xenophobia, racism, sexism), but which nonetheless agitate and divide an emergent “precariat” (Standing 2011). I hope this thesis can provide the basis for further ethnographic and comparative research.

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