Aspiration for Collective Progress: Diversity and Digital Intimacy as Practised by Alexandria Ocasio-Cortez (US), Sadiq Khan (UK) and Jagmeet Singh (Canada)
Bibliographic record
Abstract
When newly elected Canadian Prime Minister Justin Trudeau revealed his cabinet in 2015, it was lauded globally for both its gender parity and ethnic diversity. Trudeau later declared that it was important to have a ministry that ‘looked like Canada’. This led commentators in other settler colonial and immigrant nations, especially in the Global North, to ponder why such parity in terms of political representation has not been possible in their own domestic political spheres thus far. This chapter discussed three case studies that will aim to demonstrate that the political party and parliamentary structures in certain settler immigrant contexts not only enable more ‘ethnics’ to participate but also that this gives voice to the collective aspirations of their communities. As seen in the previous chapter on ethnic comedians, these aspirations go beyond the first generation’s presumed emphasis on job and financial security. Such aspirations are also reflected in the speeches and social media campaigns of culturally diverse political representatives (Khorana, 2022). In this instance, I will undertake a thematic analysis of such material obtained from the social media accounts and mainstream media coverage of Jagmeet Singh (Canada), Sadiq Khan (UK) and Alexandria Ocasio-Cortez (US). These politicians have been chosen as they carefully build a distinct identity (akin to a brand) and a following through social media platforms that set them up as not only culturally diverse but also more relatable than mainstream political representatives. Their practices of ‘digital intimacy’ constitute a kind of populism for diverse, usually young, political leaders that facilitates the channelling of collective aspirations for their followers and constituents. While there may be diverse parliamentarians in the Global North who do not subscribe to progressive views and policy platforms, they are not included in the scope of this chapter. This is because the specific interest here lies in aspiration as a complex affect associated with economic migrants and how it can be mobilised for wider public good in political discourses centred on collective identity. Introduction: types of ‘diversity’ in representation and what matters In contemporary discourses about diversity in the realms of ‘representation’ in liberal democracies, such as formal politics and mainstream media, diverse representation is commonly understood as reflecting the ethnic mix of the population (Khorana, 2020a).
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".