How to determine project completion with the Machine Classification against Coding Values widget

Relativity’s toolset isn’t just about tossing documents into bins. It’s about reading the data story your coding creates and telling you, with a confident nudge, how close you are to finishing a phase of the project. One handy beacon in that signal system is the Machine Classification against Coding Values widget. If you know how to read its bars, you gain a surprisingly clear sense of whether you’ve wrapped up a round of coding or if there’s still work to do.

What you’re looking at when you fire up the widget

Think of the widget as a quick visual audit of how well your coded documents match the relevance criteria you set. Each document or category gets a bar that represents a relevance score assigned by machine classification. The color cue you’ll notice most often is purple. Those purple bars denote documents that the system flags as relevant, according to the coding values you’ve established.

But here’s the key detail: there’s a threshold, a 50-point line that helps separate “meh” from meaningful. Bars popping above 50 suggest a strong alignment with your relevance criteria, while bars below that line are less compelling signals. The widget doesn’t just shout “more data!” or “fewer documents!” It’s telling you how much of the current coding genuinely hits the mark you care about.

So, how do you decide if the work is done? The answer the framework points to is straightforward, but like many good heuristics, it benefits from a little context.

Check for few or no purple bars over the 50 mark for relevance

Here’s the crisp point: when you scan the widget and you see only a handful of purple bars above 50—or none at all—the interpretation offered by this approach is that the current coding set isn’t generating high-relevance items. In plain terms, the set either has hit a natural ceiling of what’s relevant to the project, or it simply isn’t distinguishing enough between relevant and non-relevant material. This condition acts as a practical signal that the current coding scheme has reached a stable state for the project’s objectives, at least for the moment, and you can move forward with confidence that the objective criteria have been met—or at least have been met consistently enough to proceed.

Why this focus on relevance matters more than sheer volume or a ticking clock

You might be tempted to gauge completion by counting how many documents you’ve coded or by watching a timeline creep forward. It’s a tempting shortcut to lean on quantity or pace because those numbers are easy to grasp. But the widget’s real value isn’t about volume; it’s about quality. A flood of coded documents, all with middling relevance scores, won’t necessarily move the needle toward your project goals. Conversely, a lean set of highly relevant items can carry the day, showing you’ve captured the criteria that truly matter.

That’s why the threshold at 50 isn’t arbitrary. It’s a practical proxy for “how well does this coding hit the target?” If your purple bars rarely clear that threshold, you’re not simply looking at a lack of data—you’re facing a signal that your criteria, rules, or tagging strategy may need refinement to align with the project’s objectives. If, after thoughtful adjustment, you still don’t see bars crossing 50, that’s the moment to pause, re-evaluate your relevance definitions, and perhaps recalibrate the coding values.

Reading the signal without getting lost in the numbers

Let’s keep this grounded with a few real-world touchpoints:

• Interpreting a sparse purple signal: A sparse set of high-relevance bars often means the coding values are either too strict or not capturing the nuances the project requires. It might be time to loosen a rule, introduce a clarifying criterion, or re-train how the machine interprets certain keywords or doc types. The goal is to nudge the model toward recognizing the kind of relevance your human reviewers would affirm.

• When a lot of purple bars appear but stop short of the threshold: That’s a gentle nudge that the model is catching a broad sense of relevance, but the signal isn’t strong enough to claim completion. You might need a targeted review of edge cases, or maybe a focused set of examples to sharpen the coding guidance.

• If bars consistently spike above 50: That’s a green light that the coding values are aligning with the project objectives. Reassurance, yes, but with a caveat: it’s still wise to spot-check the adjacent bars and ensure there isn’t a blind spot in a particular category or document type.

A practical way to work with the widget

• Start with a baseline check: Run the machine classification against your current coding values and glance at the purple bars relative to 50. If you see most bars under 50, plan a quick review of your criteria and a few reference documents to calibrate what counts as relevant.

• Do a targeted sample: Pick a small, representative slice of documents and examine why certain items are flagged above or below 50. Does the recognition reflect your legitimate business or research questions? If not, adjust.

• Iterate with intention: The widget isn’t a one-and-done test. It’s a feedback loop. Make a small, purposeful adjustment, run the classification again, and compare. You’re looking for a trend toward more bars crossing the 50 mark when relevance is genuinely growing.

• Don’t ignore the rest of the data landscape: The widget focuses on relevance, but the bigger picture includes context, completeness, and alignment with broader project goals. Use the relevance signal as a guide, not as a ruler for all decisions.

Common misreadings and how to avoid them

• Confusing quantity with quality: More coded documents can look impressive, but if they don’t score above 50 in purple, they aren’t delivering the high-value signal you need. Don’t fall into the trap of chasing numbers without checking the quality of what’s being captured.

• Treating the threshold as a ceiling: Some teams worry that any bar above 50 means you’re done everywhere. In practice, it’s a checkpoint, not a final verdict. There can be areas where deeper scrutiny remains valuable even when many items clear 50.

• Forgetting human judgment: The widget is a powerful guide, but human review remains essential. The model’s signals should be calibrated to human expectations, and reviewers should verify that relevance aligns with the project’s actual aims.

A mental model you can carry into meetings

Think of the widget as a relevance compass. The purple bars are the magnetic needles pointing toward topics and document types that truly matter for the project. When the needles cluster around the 50-point line or stay below it, it’s a signal to re-check the map—revisit criteria, rephrase rules, or narrow the focus. If the needles drift confidently above 50 across many categories, you’re receiving a strong, steady indication that the current coding schema is doing what you intended.

A few practical tips to keep the flow smooth

• Keep your coding values concise and well-documented. The machine will reflect the clarity you provide, so invest a bit of time in writing crisp criteria that your team agrees on.

• Use sample verification to anchor your threshold judgments. A handful of well-understood examples can prevent misreads when the bars swing just around the 50 mark.

• Balance automation with targeted human quality checks. The widget is an excellent first pass, but spot checks by seasoned reviewers will keep the interpretation honest and aligned with objectives.

Relativity and the bigger picture

Relativity’s landscape isn’t only about pushing through a long list of documents. It’s about shaping a narrative that your project’s stakeholders can trust. The Machine Classification against Coding Values widget is one of the quiet workhorses in that toolkit. It helps you translate a tangle of data into a clean signal about relevance, which in turn informs whether the coding phase has reached a stable, defensible state or needs another round of tightening.

If you’ve ever tussled with contradictory signals—lots of activity, but not the kind that seems to matter—the widget can help you cut through the noise. It invites you to pause, reassess, and reframe how you define relevance, so your next steps are guided by data you can justify rather than assumptions you’ve outgrown.

Closing thoughts

Reading the purple bars above or below the 50 threshold isn’t a magical end-all, but it’s a practical compass for evaluating the coding phase in Relativity. It’s about quality over sheer volume, about letting the data tell you where interpretation ends and validation begins. When you see few or no bars crossing that line, you’re not just looking at numbers—you’re looking at a signal that your criteria and coding have reached a shared understanding with the project’s aims.

If you keep this approach in mind, you’ll navigate the widget’s signals with confidence. And when you couple that confidence with careful human review, you’re building a foundation that’s sturdy enough to support real decisions—ones that matter for the project’s outcomes, not just its timelines.