Digital music consumption and social capital shifts within the Cape Breton diaspora in Boston
Bibliographic record
Abstract
While ethnomusicological scholarship has begun to address Internet studies, the field has yet to amply consider digital diaspora theory. Arguing that the increasing digital aspect of social capital – defined as “the benefits individuals derive from their social relationships and interactions” (Ellison, Steinfeld, and Lampe 2010, 873) -- affects social, cultural, and musical capital in diasporic community groups, I discuss the pivotal role that social media, videosharing sites, and other Internet platforms play in connecting diasporic communities. I develop a hybrid ethnographic fieldwork model for examining contemporary diasporas’ music consumption and production that builds upon Putnam’s (2000) work on social capital, Song’s (2009) analysis of virtual communities, Brinkerhoff’s (2009) conceptualization of digital diaspora, Turkle’s (2011) fieldwork on technology’s impact on social interaction, Sparling’s (2006) conception of cultural capital in Gaelic Cape Breton, and O’Hara and Brown’s (2006) examination of music consumption. To address the high value of music production and consumption in Cape Breton culture, I introduce the concept of musical capital. I define this as arts currency, both tangible and intangible, which can be procured, acquired, or shared, as a more specific way to discuss the shifts in participation and consumption documented in my fieldwork in 2014-15, conducted both online and at the Canadian-American Club in Watertown, Massachusetts. Forms of musical capital analyzed include Skype music lessons, songs of diasporic longing, fiddle sessions, online videos, and in-person performances. I conclude that the online availability of one’s culture has long-range effects for community participation by non-musicians. While artists still gather in person to practice and perform, the greater diasporic community can now interact with other members online and virtually experience their culture, though the personal social capital benefits are not equal to in-person interactions. These changes reflect a larger social capital shift within contemporary American society and acknowledge the impact of the increased use of, and reliance upon, Internet platforms as a means for creating, consuming, and disseminating musical and cultural capital.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".