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Record W4414203406 · doi:10.51644/bcs015

When Goodwill Turns Bad: The Case of Lightspeed Commerce Inc.

2025· report· en· W4414203406 on OpenAlex
Bruce J. McConomy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGoodwillAmortizationProfitability indexEarningsDepreciation (economics)Earnings before interest, taxes, depreciation, and amortizationBook valueStock exchange

Abstract

fetched live from OpenAlex

Lightspeed Commerce Inc. was once among the darlings of the Toronto Stock Exchange (TSX), with a peak share price of over $150 in 2021. But the high rate of growth was not sustainable for this Canadian-based provider of point-of-sale and e-commerce software, and its share price dropped by more than 85 percent by the middle of 2022. The 2025 results for Lightspeed included a write-down of Goodwill totalling US$556.4 million, an overall net loss of US$667.3 million and an adjusted Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) profitability for the year of US$53.7 million. The case requires participants to discuss the difference between intangible assets and goodwill, assess the nature of goodwill impairment, discuss the nature of pro forma measures of income under International Financial Reporting Standards (IFRS), and inform potential investors whether or not the future is promising for Lightspeed.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.276
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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