Crowdsourcing and small business challenges: how to leverage crowdsourcing benefits in the information and communications technology industry
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) make significant contributions to economic growth; however, they face multiple challenges that inhibit their success. It is argued in this paper that crowdsourcing could be leveraged to alleviate many of their challenges particularly in the information and communications technology (ICT) industry. Our findings show that Canadian SMEs are using crowdsourcing less than their American counterparts. However, Canadian SMEs have a more optimistic outlook towards crowdsourcing impacts. Additionally, ten categories of Canadian SME challenges were identified through a review of literature. Finally, a compelling argument for leveraging crowdsourcing to address eight of these challenge categories is made, relying on existing literature and case studies of SME's utilisation of crowdsourcing. SME owners could leverage the findings from this paper to improve their chance of survival. Policy makers could also see benefits in guiding the design of new policies aimed at supporting small businesses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".