Can the Internet Help? How Immigrant Women from China Get Jobs: A Survey on PRC Immigrants’ Employment Status in Canada
Bibliographic record
Abstract
Introduction This chapter studies how the Internet can ease access to skilled jobs for Chinese women immigrants in Canada and where it fails. Labour markets develop mechanisms that segment the working populace into those who are seen as more and less worthy of the good jobs. Gender and the attribution of who are indigenous or intruders are key divisions between contenders for prime positions. In North America, entrenched divisions between women and men, immigrants and locals, are accompanied by definitions of who possess the required characteristics for job holders. With the pay, prestige, and culture of jobs differing, good jobs are contested by means of definitions of who is worthy. Institutional theory conjectures that these rationales are socially constructed, shared beliefs that are not questioned. Newcomers on the block, especially women immigrants, fail to get good jobs because they fit poorly into the institutional environment. They do not have the power to cross the boundaries into the desirable jobs. A number of writers posit how the Internet can break through the institutional allocation of gender roles and work roles. Post modernist theory claims that through the need for new information, the knowledge society flattens traditional hierarchies and rigid subjectivities underlying gendered and racialized jobs and restructures the institutional order (Bell 1973; Castells 1996; Webster 2005). Some writers posit that the Internet builds networks and can create new forms of useful contacts, or social capital. They contend that most immigrants lack information about the new country and they do not have much social capital. Caidi and Allard (2005) maintain that access to the Internet can help them build social networks. However, there are indications that the Internet does not change the old order (Gamba & Kleiner 2001) and most of the so-called networking websites build their connections on existing social relations so that immigrants will face the same problems on the Internet that they face in real life. Most networks form around similar others (Ibarra 1992; McPherson et al. 2001). We argue that the Internet affects social structures indirectly by enabling job seekers to apply in ways that minimize control of gatekeepers. Gatekeepers to jobs are part of the gendered and racialized segmented labour force, and we posit that the Internet can help reduce the gate keeping effects through randomness in screening from large numbers of applicants.
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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 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".