A Sense of Belonging: Social Media Use of Latin American migrants in Australia
Bibliographic record
Abstract
This chapter focuses on how social media is used as a new form of agency to assist Latin Americans in their process of migration in Australia. In particular, the chapter examines a Facebook-linked webpage that publishes inspiring stories (expressions of belonging, their success and their contribution to the Australian society) of Latin Americans who have succeeded as migrants in different parts of Australia. Members use the digital platform to find support and inspiration. This study explored the narrative of their members towards these stories by using content analysis methodology (CA). We analysed 63 messages and comments posted in Spanish and English on the Facebook-linked webpage. To incorporate the insights from the literature on migration, we focus upon three key phases of migration: pre-migrants, post-migrants and settled migrants. For the purpose of this study we will use the term Latin Americans but consciously knowing that we are not putting every Latin American under the same umbrella, as every Latin American country has its own differences. Latin American immigrants in Australia are a small population in comparison to the one in the USA or Canada. This, therefore, makes this study a first academic investigation on the use of Facebook by Latin American immigrants in some parts of Australia. We address two main research questions: how the narratives of identity and belonging support identities and cultural practices within the Latin American communities; and how those stories impact, motivate and enhance the participants' ability to adapt to the new conditions they face as migrants, in particular new arrivals. The present work opens new venues for future comparative analysis of immigrant narratives of their experiences under new migration policies between Latin Americans living in Australia and in other countries such as the USA that could be of interest to researchers on migration and social media.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".