Bringing Indigenous Voices to the Workplace
Bibliographic record
Abstract
Abstract \nI question how Indigenous identity, relationships, and the workplace environment affect an Indigenous employee’s experience in the workplace. I had the pleasure and opportunity of hearing and now sharing the stories of seven Indigenous alumni from the University of Waterloo and Wilfred Laurier University, who live and work in southwestern Ontario. \n\tResearch shows that Indigenous employees are unsatisfied, have a high attrition rate, and lack representation within the workplace (Hsiao, Auld & Ma, 2014; Liao, Chuang, & Joshi, 2008; Racine, 2016; Scott, Heathcote & Gruman, 2011). My research aimed to understand the sources of satisfaction and retention for Indigenous employees within the workplace. To do this, I had conversations about their experiences within the organizations that they work. \nThe themes discussed in the conversations covered cultural identity, workplace relationships, communication, and workplace environment. The first theme surrounding identity comes from my journey surrounding my indigeneity , my changing identity, and the impact it has had on my experience throughout life, in school, and within the workplace. The second theme, communication, comes from the understanding that communication is a part of every organizational process and is related to task performance, job satisfaction, and trust. (Adair, Buchan, Chen, & Liu, 2016). The third theme, relationships, is a big part of the workplace experience for both Indigenous and non-Indigenous people (Burgess & Dyer, 2009; Venkataramani, Labianca, & Grosser, 2013). The fourth and final theme, the workplace environment, is taken into consideration as it is another large part of the experience of Indigenous employees within the workplace. \nIn order to have a full understanding of the experience of an Indigenous employee, it was necessary to use Indigenous research methods. I had four relational conversations with each participant that ranged from 45 minutes to an hour in length and took place in the majority of my participant’s workplaces. It was vital that I had a minimum of four conversations to establish a meaningful relationship with my participants. The conversations were recorded, and after speaking with all seven participants, I listened to the recorded conversations, read the transcripts, and created gemstone stories by re-storying what was shared in the conversations with the Indigenous employees. \nAs I began to listen and re-story what I heard through the conversations, a number of themes emerged. To understand and organize all of these themes, I developed what I am calling the Wholistic Organizational Framework modeled on Kathy Absolon’s Wholistic Theory Framework (Absolon, 2010). After placing the themes within the framework, I began to make meaning of everything that came to the surface. \nThrough hearing the Indigenous employees' experiences in the workplace, as a community member, I am hoping that I can share my findings with organizations to create a positive, supportive workplace experience where Indigenous employees thrive, have a sense of belonging, and know that their voices are being heard. As an Indigenous scholar, I hope to contribute to decolonizing, restoring, and revitalizing Indigenous research methods.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.023 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".